springer handbook ofcomputationalintelligence3a978-3-662-43505-2%2f… · prising each volume. the...
TRANSCRIPT
Springer Handbookof Computational Intelligence
Springer Handbooks providea concise compilation of approvedkey information on methods ofresearch, general principles, andfunctional relationships in physicaland applied sciences. The world’sleading experts in the fields ofphysics and engineering will be as-signed by one or several renownededitors to write the chapters com-prising each volume. The contentis selected by these experts fromSpringer sources (books, journals,online content) and other systematicand approved recent publications ofscientific and technical information.
The volumes are designed to beuseful as readable desk referencebook to give a fast and comprehen-sive overview and easy retrieval ofessential reliable key information,including tables, graphs, and bibli-ographies. References to extensivesources are provided.
HHandbookSpringer
of Computational IntelligenceJanusz Kacprzyk, Witold Pedrycz (Eds.)
With 534 Figures and 115 Tables
K
EditorsJanusz KacprzykPolish Academy of SciencesSystems Research Inst.ul. Newelska 601-447 Warsaw, [email protected]
Witold PedryczUniversity of AlbertaDep. Electrical and Computer Engineering116 Street 9107T6J 2V4, Edmonton, Alberta, [email protected]
ISBN: 978-3-662-43504-5 e-ISBN: 978-3-662-43505-2DOI 10.1007/978-3-662-43505-2Springer Dordrecht Heidelberg London New York
Library of Congress Control Number: 2015936335
© Springer-Verlag Berlin Heidelberg 2015This work is subject to copyright. All rights are reserved, whether the wholeor part of the material is concerned, specifically the rights of translation,reprinting, reuse of illustrations, recitation, broadcasting, reproduction onmicrofilm or in any other way, and storage in data banks. Duplication ofthis publication or parts thereof is permitted only under the provisions ofthe German Copyright Law of September 9, 1965, in its current version,and permission for use must always be obtained from Springer. Violationsare liable to prosecution under the German Copyright Law.The use of general descriptive names, registered names, trademarks,etc. in this publication does not imply, even in the absence of a specificstatement, that such names are exempt from the relevant protective lawsand regulations and therefore free for general use.
Production and typesetting: le-tex publishing services GmbH, LeipzigSenior Manager Springer Handbook: Dr. W. Skolaut, HeidelbergTypography and layout: schreiberVIS, SeeheimIllustrations: Hippmann GbR, SchwarzenbruckCover design: eStudio Calamar Steinen, BarcelonaCover production: WMXDesign GmbH, HeidelbergPrinting and binding: Printer Trento s.r.l., Trento
Printed on acid free paper
Springer is part of Springer Science+Business Media (www.springer.com)
V
Preface
We are honored and happy to be able to make available this Springer Handbook ofComputational Intelligence, a large and comprehensive account of both the state-of-the-art of the research discipline, complemented with some historical remarks, mainchallenges, and perspectives of the future. To follow a predominant tradition, we havedivided this Springer Handbook into parts that correspond to main fields that are meantto constitute the area of computational intelligence, that is, fuzzy sets theory and fuzzylogic, rough sets, evolutionary computation, neural networks, hybrid approaches andsystems, all of them complemented with a thorough coverage of some foundationalissues, methodologies, tools, and techniques.
We hope that the handbook will serve as an indispensable and useful source ofinformation for all readers interested in both the theory and various applications ofcomputational intelligence. The formula of the Springer Handbook as a convenientsingle-volume publication project should help the potential readers find a proper toolor technique for solving their problems just by simply browsing through a clearlycomposed and well-indexed contents. The authors of the particular chapters, who arethe best known specialists in their respective fields worldwide, are the best assurancefor the handbook to serve as an excellent and timely reference.
On behalf of the entire computational intelligence community, we wish to expresssincere thanks, first of all, to the Part Editors responsible for the scope, authors, andcomposition of the particular parts for their great job to arrange the most appropriatetopics, their coverage, and identify expert authors. Second, we wish to thank all theauthors for their great contributions in the sense of clarity, comprehensiveness, novelty,vision, and – above all – understanding of the real needs of readers of diverse interests.
All that efforts would not end up with the success without a total and multifacetedpublisher’s dedication and support. We wish to thank very much Dr. Werner Skolaut,Ms. Constanze Ober, and their collaborators from Springer, Heidelberg, and le-texpublishingGmbH, Leipzig, respectively, for their extremely effective and efficient han-dling of this huge and difficult project.
September 2014Janusz Kacprzyk WarsawWitold Pedrycz Edmonton
VII
About the Editors
Janusz Kacprzyk graduated from the Department of Electronics, Warsaw Univer-sity of Technology, Poland with an MSc in Automatic Control, a PhD in SystemsAnalysis and a DSc (Habilitation) in Computer Science from the Polish Academy ofSciences. He is Professor of Computer Science at the Systems Research Institute, Pol-ish Academy of Sciences, Professor of Computerized Management Systems at WIT –Warsaw School of Information Technology, and Professor of Automatic Control atPIAP – Industrial Institute of Automation and Measurements, in Warsaw, Poland, andDepartment of Electrical and Computer Engineering, Cracow University of Technol-ogy, Poland. He is the author of 5 books, (co)editor of ca. 70 volumes, (co)author ofca. 500 papers. He is Editor-in-Chief of 6 book series and of 2 journals, and on theEditorial Boards of more than 40 journals.
Witold Pedrycz is a Professor and Canada Research Chair (CRC) in ComputationalIntelligence in the Department of Electrical and Computer Engineering, Universityof Alberta, Edmonton, Canada. He is also with the Systems Research Institute of thePolish Academy of Sciences, Warsaw. He also holds an appointment of special profes-sorship in the School of Computer Science, University of Nottingham, UK. His mainresearch directions involve computational intelligence, fuzzy modeling and granularcomputing, knowledge discovery and data mining, fuzzy control, pattern recognition,knowledge-based neural networks, relational computing, and software engineering. Hehas published numerous papers and is the author of 15 research monographs coveringvarious aspects of computational intelligence, data mining, and software engineering.He currently serves as an Associate Editor of IEEE Transactions on Fuzzy Systemsand is a member of a number of Editorial Boards of other international journals.
IX
About the Part Editors
Cesare Alippi Part D
Politecnico di MilanoDip. Elettronica, Informazione eIngegneria20133 Milano, [email protected]
Cesare Alippi received his PhD in 1995 from Politecnico di Milano, Italy. Currently,he is Professor at the same institution. He has been a visiting researcher at UCL (UK),MIT (USA), ESPCI (F), CASIA (RC), USI (CH). Alippi is an IEEE Fellow, Vice-President Education of the IEEE Computational Intelligence Society, Associate Editorof the IEEE Computational Intelligence Magazine, Past Editor of the IEEE-TIM andIEEE-TNN(LS). In 2004 he received the IEEE Instrumentation and MeasurementSociety Young Engineer Award and in 2013 the IBM Faculty Award. His currentresearch focuses on learning in non-stationary environments and intelligence forembedded systems. He holds 5 patents, has published 1 monograph book, 6 editedbooks and about 200 papers in international journals and conference proceedings.
Thomas Bartz-Beielstein Part E
Cologne University of Applied SciencesFaculty of Computer Science andEngineering Science51643 Gummersbach, [email protected]
Thomas Bartz-Beielstein is a Professor of Applied Mathematics at Cologne Universityof Applied Sciences (CUAS). His expertise lies in optimization, simulation, andstatistical analysis of complex real-world problems. He has more than 100 publicationson computational intelligence, optimization, simulation, and experimental research.He has been on the program committees of several international conferences andorganizes the prestigious track Evolutionary Computation in Practice at GECCO. Hisbooks on experimental research are considered as milestones in this emerging field.He is speaker of the research center Computational Intelligence plus at CUAS andhead of the SPOTSeven team.
Christian Blum Part F
University of the Basque CountryDep. Computer Science and ArtificialIntelligence20018 San Sebastian, [email protected]
Christian Blum holds a Master’s Degree in Mathematics (1998) fromthe University of Kaiserslautern, Germany, and a PhD degree in AppliedSciencies (2004) from the Free University of Brussels, Belgium. Hecurrently occupies a permanent post as Ikerbasque Research Professor atthe University of the Basque Country, San Sebastian, Spain. His researchinterests include the development of swarm intelligence techniques andthe combination of metaheuristics with exact approaches for solvingdifficult optimization problems. So far he has co-authored about 150research papers.
Oscar Castillo Part G
Tijuana Institute of Technology22379 Tijuana, [email protected]
Oscar Castillo holds the Doctor in Science degree in Computer Sciencefrom the Polish Academy of Sciences. He is a Professor of ComputerScience in the Graduate Division, Tijuana Institute of Technology,Tijuana, Mexico. In addition, he serves as Research Director ofComputer Science. Currently, he is Vice-President of HAFSA (HispanicAmerican Fuzzy Systems Association) and served as President of IFSA(International Fuzzy Systems Association). He belongs to the MexicanResearch System with level II and is also a member of NAFIPS, IFSA,and IEEE. His research interests are in type-2 fuzzy logic, fuzzy control,and neuro-fuzzy and genetic-fuzzy hybrid approaches.
X About the Part Editors
Carlos A. Coello Coello Part E
CINVESTAV-IPNDep. ComputaciónD.F. 07300, México, [email protected]
Carlos A. Coello Coello received a PhD in Computer Science from Tulane Universityin 1996. He has made pioneering contributions to the research area currently knownas evolutionary multi-objective optimization, mainly regarding the development ofnew algorithms. He is currently Professor at the Computer Science Departmentof CINVESTAV-IPN (Mexico City, México). He has co-authored more than 350publications (his h-index is 62). He is Associate Editor of several journals, includingIEEE Transactions on Evolutionary Computation and Evolutionary Computation. Hehas received Mexico’s National Medal of Science in Exact Sciences and the IEEEKiyo Tomiyasu Award. He is also an IEEE Fellow.
Bernard De Baets Part A
Ghent UniversityDep. Mathematical Modelling, Statisticsand Bioinformatics9000 Ghent, [email protected]
Bernard De Baets (1966) holds an MSc degree in Mathematics, a postgraduate degreein Knowledge Technology, and a PhD degree in Mathematics. He is a full professor atUGent (Belgium), where he leads KERMIT, an interdisciplinary team in mathematicalmodeling, having delivered 50 PhD graduates to date. His bibliography comprisesnearly 400 journal papers, 60 book chapters, and 300 conference contributions. Heacts as Co-Editor-in-Chief (2007) of Fuzzy Sets and Systems. He is a recipient ofa Government of Canada Award, Honorary Professor of Budapest Tech (Hungary),Fellow of the International Fuzzy Systems Association, and has been nominated forthe Ghent University Prometheus Award for Research.
Roderich Groß Part F
University of SheffieldDep. Automatic Control and SystemsEngineeringSheffield, S1 3JD, [email protected]
Roderich Groß received a Diploma degree in Computer Science fromTU Dortmund University in 2001 and a PhD degree in EngineeringSciences from the Université libre de Bruxelles in 2007. From 2005 to2009 he was a fellow of the Japan Society for the Promotion of Science,a Research Associate at the University of Bristol, a Marie Curie Fellowat Unilever, and a Marie Curie Fellow at EPFL. Since 2010 he has beenwith the Department of Automatic Control and Systems Engineering atthe University of Sheffield, where he is currently Senior Lecturer. Hisresearch interests include evolutionary and distributed robotics. He hasauthored over 60 publications on these topics. He is a Senior Member ofthe IEEE.
Enrique Herrera Viedma Part B
University of GranadaDep. Computer Science and ArtificialIntelligence18003 Granada, [email protected]
Enrique Herrera-Viedma received his PhD degree in Computer Sciencefrom Granada University in 1996. He is Professor at Granada Universityin the Depaartment of Computer Science and Artificial Intelligence anda member of the BoG in IEEE SMC. His interest topics are computingwith words, fuzzy decision making, consensus, aggregation, socialmedia, recommender systems, libraries, and bibliometrics. His h-indexis 44 and presents over 7000 citations (WoS). In 2014 he was identifiedas Highly Cited Researcher by Thomson Reuters and Top Author inComputer Science according to Microsoft Academic Search.
Luis Magdalena Part B
European Centre for Soft Computing33600 Mieres, [email protected]
Luis Magdalena received the MS and PhD degrees in Telecommunication Engineeringfrom the Technical University of Madrid, Spain, in 1988 and 1994. He has beenAssistant (1990–1995) and Associate Professor (1995–2006) in Computer Science atthe Technical University of Madrid. Since 2006 he has been Director General of theEuropean Center for Soft Computing. His research interests include soft computingand its application. He has authored over 150 publications in the field. He has beenPresident of the European Society for Fuzzy Logic and Technologies (2001–2005),Vice-President of the International Fuzzy Systems Association (2007–2011), andmember of the IEEE Computational Intelligence Society AdCom (2011–2013).
About the Part Editors XI
Jörn Mehnen Part E
Cranfield UniversityManufacturing Dep.Cranfield, MK43 0AL, [email protected]
Dr Jörn Mehnen is Reader in Computational Manufacturing at Cranfield University,UK and Privatdozent at TU Dortmund, Germany. He is also Deputy Director of theEPSRC Centre in Through-life Engineering Services at Cranfield University. Hisresearch activities are in real-world applications of computer sciences in mechanicalengineering with special focus on evolutionary optimization, cloud manufacturing,and additive manufacturing.
Patricia Melin Part G
Tijuana Institute of TechnologyDep. Computer ScienceChula Vista, CA 91909, [email protected]
Patricia Melin holds the Doctor in Science degree in Computer Sciencefrom the Polish Academy of Sciences. She has been a Professor of Com-puter Science in the Graduate Division, Tijuana Institute of Technology,Tijuana, Mexico since 1998. She serves as Director of Graduate Studies inComputer Science. Currently, she is Vice President of HAFSA (HispanicAmerican Fuzzy Systems Association). She is the founding Chair of theMexican Chapter of the IEEE Computational Intelligence Society. Sheis member of NAFIPS, IFSA, and IEEE and belongs to the MexicanResearch System with level III. Her research interests are in type-2 fuzzylogic, modular neural networks, pattern recognition, fuzzy control, andneuro-fuzzy and genetic-fuzzy hybrid approaches. She has published over200 journal papers, 6 authored books, 20 edited books, and 200 papers inconference proceedings.
Peter Merz Part E
University of Applied Sciences and Arts,HannoverDep. Business Administration andComputer Science30459 Hannover, [email protected]
Peter Merz received his PhD degree in Computer Science from theUniversity of Siegen, Germany in 2000. Since 2009, he has beenProfessor at the University of Applied Sciences and Arts in Hannover.He is a well-known scientist in the field of evolutionary computationand meta-heuristics. His research interests center on fitness landscapesof combinatorial optimization problems and their analysis.
Radko Mesiar Part A
STU in BratislavaDep. Mathematics and DescriptiveGeometry813 68 Bratislava, [email protected]
Radko Mesiar received his PhD from Comenius University, Faculty of Mathematicsand Physics, in 1979. He has been a member of the Department of Mathematics in theFaculty of Civil Engineering, STU Bratislava since 1978. He received his DSc in 1996from the Czech Academy of Sciences. He has been a full professor since 1998. He is afellow member of the Institute of Information and Automation at the Czech Academyof Sciences and of IRAFM, University of Ostrava (Czech Republic). He is co-authorof two scientific monographs and five edited volumes. He is the author of more than200 papers in WOS in leading journals. He is the co-founder of conferences AGOP,FSTA, ABLAT, and ISCAMI.
Frank Neumann Part E
The University of AdelaideSchool of Computer ScienceAdelaide, SA 5005, [email protected]
Frank Neumann received his diploma and PhD from the University of Kiel in2002 and 2006, respectively. Currently, he is an Associate Professor and leaderof the Optimisation and Logistics Group at the School of Computer Science, TheUniversity of Adelaide, Australia. He is the General Chair of ACM GECCO 2016.He is Vice-Chair of the IEEE Task Force on Theoretical Foundations of Bio-InspiredComputation, and Chair of the IEEE Task Force on Evolutionary Scheduling andCombinatorial Optimization. In his work, he considers algorithmic approaches andfocuses on theoretical aspects of evolutionary computation as well as high impactapplications in the areas of renewable energy, logistics, and sports.
XII About the Part Editors
Marios Polycarpou Part D
University of CyprusDep. Electrical and ComputerEngineering and KIOS Research Centerfor Intelligent Systems and Networks1678 Nicosia, [email protected]
Marios Polycarpou is Professor of Electrical and Computer Engineeringand the Director of the KIOS Research Center for Intelligent Systemsand Networks at the University of Cyprus. His research expertise is inthe areas of intelligent systems and control, computational intelligence,fault diagnosis, cooperative and adaptive control, and distributed agents.He is a Fellow of the IEEE. He has participated in more than 60 researchprojects/grants, funded by several agencies and industries in Europe andthe United States. In 2011, he was awarded the prestigious EuropeanResearch Council (ERC) Advanced Grant.
Günther Raidl Part E
Vienna University of TechnologyInst. Computer Graphics and Algorithms1040 Vienna, [email protected]
Günther Raidl is Professor at the Vienna University of Technology,Austria, and heads the Algorithms and Data Structures Group. Hereceived his PhD in 1994 and completed his Habilitation in PracticalComputer Science in 2003. In 2005 he received a professorshipposition for combinatorial optimization. His research interests includealgorithms and data structures in general and combinatorial optimizationin particular, with a specific focus on metaheuristics, mathematicalprogramming, and hybrid optimization approaches.
Oliver Schütze Part E
CINVESTAV-IPNDep. ComputaciónD.F. 07300, México, [email protected]
Oliver Schütze received a PhD in Mathematics from the University of Paderborn,Germany in 2004. He is currently Professor at Cinvestav-IPN in Mexico City(Mexico). His research interests focus on numerical and evolutionary optimizationwhere he addresses scalar and multi-objective optimization problems. He has co-edited 5 books and is co-author of more than 90 papers. He is a co-founder of SON(Set Oriented Numerics) and founder of the NEO (Numerical and EvolutionaryOptimization) workshop series.
Roman Słowiński Part C
Poznań University of TechnologyInst. Computing Science60-965 Poznań, [email protected]
Roman Słowinski is Professor and Founding Chair of the Laboratory of IntelligentDecision Support Systems at Poznan University of Technology. He is Academicianand President of the Poznan Branch of the Polish Academy of Sciences and amember of Academia Europaea. In his research, he combines operations research andcomputational intelligence. He is renowned for his seminal research on using roughsets in decision analysis. He was laureate of the EURO Gold Medal (1991) and wonthe 2005 Prize of the Foundation for Polish Science. He is Doctor Honoris Causaof Polytech’Mons (2000), the University Paris Dauphine (2001), and the TechnicalUniversity of Crete (2008).
Carsten Witt Part E
Technical University of DenmarkDTU Compute, Algorithms, Logic andGraphs2800 Kgs., Lyngby, [email protected]
Carsten Witt is Associate Professor at the Technical University of Den-mark. He received his PhD in Computer Science from the TechnicalUniversity of Dortmund in 2004. His main research interests are the the-oretical aspects of nature-inspired algorithms, in particular evolutionaryalgorithms, ant colony optimization and particle swarm optimization. Heis a member of the Editorial Boards of Evolutionary Computation andTheoretical Computer Science and has co-authored a textbook.
About the Part Editors XIII
Yiyu Yao Part C
University of ReginaDep. Computer ScienceRegina, Saskatchewan, S4S 0A2, [email protected]
Yiyu Yao is Professor of Computer Science in the Department ofComputer Science, the University of Regina, Canada. His researchinterests include three-way decisions, rough sets, fuzzy sets, intervalsets, granular computing, information retrieval, Web intelligence, anddata mining. He is currently working on a triarchic theory of granularcomputing, a theory of three-way decisions and generalized rough sets.
XV
List of Authors
Enrique AlbaUniversidad de MalagaE.T.S.I. InformáticaCampus de Teatinos (3.2.12)29071 Málaga, Spaine-mail: [email protected]
Jose M. AlonsoEuropean Centre for Soft ComputingCognitive Computing33600 Mieres, Spaine-mail: [email protected]
Jhon Edgar AmayaUniversidad Nacional Experimental del TáchiraDep. Electronic EngineeringAv. Universidad. ParamilloSan Cristóbal, Venezuelae-mail: [email protected]
Plamen P. AngelovLancaster UniversitySchool of Computing and CommunicationsBailrigg, Lancaster, LA1 4YW, UKe-mail: [email protected]
Dirk V. ArnoldDalhousie UniversityFaculty of Computer Science6050 University AvenueHalifax, Nova Scotia, B3H 4R2, Canadae-mail: [email protected]
Anne AugerUniversity Paris-Sud OrsayCR InriaLRI (UMR 8623)91405 Orsay Cedex, Francee-mail: [email protected]
Davide BacciuUniversità di PisaDip. InformaticaL.Go B. Pontecorvo, 356127 Pisa, Italye-mail: [email protected]
Michał BaczynskiUniversity of SilesiaInst. MathematicsBankowa 1440-007 Katowice, Polande-mail: [email protected]
Edurne BarrenecheaUniversidad Pública de NavarraDep. Automática y Computación31006 Pamplona (Navarra), Spaine-mail: [email protected]
Thomas Bartz-BeielsteinCologne University of Applied SciencesFaculty of Computer Science and EngineeringScienceSteinmüllerallee 151643 Gummersbach, Germanye-mail: [email protected]
Lubica BenuskovaUniversity of OtagoDep. Computer Science133 Union Street East9016 Dunedin, New Zealande-mail: [email protected]
Dirk BiermannTU Dortmund UniversityDep. Mechanical EngineeringBaroper Str. 30344227 Dortmund, Germanye-mail: [email protected]
Sašo BlažičUniversity of LjubljanaFaculty of Electrical EngineeringTržaška 251000 Ljubljana, Sloveniae-mail: [email protected]
Christian BlumUniversity of the Basque CountryDep. Computer Science and Artificial IntelligencePaseo Manuel Lardizabal 120018 San Sebastian, Spaine-mail: [email protected]
XVI List of Authors
Andrea BobbioUniversità del Piemonte OrientaleDiSit – Computer Science SectionViale Teresa Michel, 1115121 Alessandria, Italye-mail: [email protected]
Josh BongardUniversity of VermontDep. Computer Science33 Colchester Ave.Burlington, VT 05405, USAe-mail: [email protected]
Piero P. BonissonePiero P. Bonissone Analytics, LLC3103 28th StreetSan Diego, CA 92104, USAe-mail: [email protected]
Dario BruneoUniversita’ di MessinaDip. Ingegneria Civile, InformaticaContrada di Dio – S. Agata98166 Messina, Italye-mail: [email protected]
Alberto Bugarín DizUniversity of Santiago de CompostelaResearch Centre for Information Technologies15782 Santiago de Compostela, Spaine-mail: [email protected]
Humberto BustinceUniversidad Pública de NavarraDep. Automática y Computación31006 Pamplona (Navarra), Spaine-mail: [email protected]
Martin V. ButzUniversity of TübingenComputer Science, Cognitive ModelingSand 1472076 Tübingen, Germanye-mail: [email protected]
Alexandre CampoUniversité Libre de BruxellesUnit of Social EcologyBoulevard du triomphe,Campus de la Plaine1050 Brussels, Belgiume-mail: [email protected]
Angelo CangelosiPlymouth UniversityCentre for Robotics and Neural SystemsDrake CircusPlymouth, PL4 8AA, UKe-mail: [email protected]
Robert CarreseLEAP Australia Pty. Ltd.Clayton North, Australiae-mail: [email protected]
Ciro CastielloUniversity of BariDep. Informaticsvia E. Orabona, 470125 Bari, Italye-mail: [email protected]
Oscar CastilloTijuana Institute of TechnologyCalzada Tecnolo-gico s/n22379 Tijuana, Mexicoe-mail: [email protected]
Davide CerottiPolitecnico di MilanoDip. Elettronica, Informazione e BioingegneriaVia Ponzio 34/520133 Milano, Italye-mail: [email protected]
Badong ChenXi’an Jiaotong UniversityInst. Artificial Intelligence and Robotics710049 Xi’an, Chinae-mail: [email protected]
Ke ChenThe University of ManchesterSchool of Computer ScienceG10 Kilburn Building, Oxford RoadManchester, M13 9PL, UKe-mail: [email protected]
Davide CiucciUniversity of Milano-BicoccaDep. Informatics, Systems and Communicationsviale Sarca 336/1420126 Milano, Italye-mail: [email protected]
List of Authors XVII
Carlos A. Coello CoelloCINVESTAV-IPNDep. ComputaciónAv. Instituto Politécnico Nacional No. 2508, Col.San Pedro ZacatencoD.F. 07300, México, Mexicoe-mail: [email protected]
Chris CornelisGhent UniversityDep. Applied Mathematics and Computer ScienceKrijgslaan 281 (S9)9000 Ghent, Belgiume-mail: [email protected]
Nikolaus CorrellUniversity of Colorado at BoulderDep. Computer ScienceBoulder, CO 80309, USAe-mail: [email protected]
Carlos Cotta PorrasUniversidad de MálagaDep. Lenguajes y Ciencias de la ComputaciónAvda Louis Pasteur, 3529071 Málaga, Spaine-mail: [email protected]
Damien CoyleUniversity of UlsterIntelligent Systems Research CentreNorthland RdDerry, Northern Ireland, BT48 7JL, UKe-mail: [email protected]
Guy De TréGhent UniversityDep. Telecommunications andInformation ProcessingSint-Pietersnieuwstraat 419000 Ghent, Belgiume-mail: [email protected]
Kalyanmoy DebMichigan State UniversityDep. Electrical and Computer Engineering428 S. Shaw LaneEast Lansing, MI 48824, USAe-mail: [email protected]
Clarisse DhaenensUniversity of LilleCRIStAL laboratoryM3 building – Cité scientifique59655 Villeneuve d’Ascq Cedex, Francee-mail: [email protected]
Luca Di GasperoUniversità degli Studi di UdineDip. Ingegneria Elettrica,Gestionale e Meccanicavia delle Scienze 20833100 Udine, Italye-mail: [email protected]
Didier DuboisUniversité Paul SabatierIRIT – Equipe ADRIA118 route de Narbonne31062 Toulouse Cedex 9, Francee-mail: [email protected]
Antonio J. Fernández LeivaUniversidad de MálagaDep. Lenguajes y Ciencias de la ComputaciónAvda Louis Pasteur, 3529071 Málaga, Spaine-mail: [email protected]
Javier FernándezUniversidad Pública de NavarraDep. Automática y Computación31006 Pamplona (Navarra), Spaine-mail: [email protected]
Martin H. FischerUniversity of PotsdamPsychology Dep.Karl-Liebknecht-Str. 24/2514476 Potsdam OT Golm, Germanye-mail: [email protected]
János C. FodorÓbuda UniversityDep. Applied MathematicsBécsi út 96/b1034 Budapest, Hungarye-mail: [email protected]
XVIII List of Authors
Jairo Alonso GiraldoUniversidad de los AndesDep. Electrical and Electronics EngineeringCra 1Este # 19A-40111711 Bogotá, Colombiae-mail: [email protected]
Siegfried GottwaldLeipzig UniversityInst. PhilosophyBeethovenstr. 1504107 Leipzig, Germanye-mail: [email protected]
Salvatore GrecoUniversity of CataniaDep. Economics and BusinessCorso Italia 5595129 Catania, Italye-mail: [email protected]
Marco GribaudoPolitecnico di MilanoDip. Elettronica, Informazione e BioingegneriaVia Ponzio 34/520133 Milano, Italye-mail: [email protected]
Roderich GroßUniversity of SheffieldDep. Automatic Control and Systems EngineeringMappin StreetSheffield, S1 3JD, UKe-mail: [email protected]
Jerzy W. Grzymala-BusseUniversity of KansasDep. Electrical Engineering and Computer Science3014 Eaton Hall, 1520 W. 15th St.Lawrence, KS 66045-7621, USAe-mail: [email protected]
Hani HagrasUniversity of EssexThe Computational Intelligence CentreWivenhoe ParkColchester, CO4 3SQ, UKe-mail: [email protected]
Heiko HamannUniverstity of PaderbornDep. Computer ScienceZukunftsmeile 133102 Paderborn, Germanye-mail: [email protected]
Thomas HammerlWestbahnstraße 25/1/71070 Vienna, Austriae-mail: [email protected]
Julie HamonIngenomixDep. Research and DevelopmentPole de Lanaud87220 Boisseuil, Francee-mail: [email protected]
Nikolaus HansenUniversitè Paris-SudMachine Learning and Optimization Group (TAO)Rue Noetzlin91405 Orsay Cedex, Francee-mail: [email protected]
Mark W. HauschildUniversity of Missouri–St. LouisDep. Mathematics and Computer Science1 University BlvdSt. Louis, MO 314-972-2419, USAe-mail: [email protected]
Sebastien HéliePurdue UniversityDep. Psychological Sciences703 Third StreetWest Lafayette, IN 47907-2081, USAe-mail: [email protected]
Jano I. van HemertOptosQueensferry House, Carnegie Business ParkDunfermline, KY11 8GR, UKe-mail: [email protected]
Holger H. HoosUniversity of British ColumbiaDep. Computer Science2366 Main MallVancouver, BC V6T 1Z4, Canadae-mail: [email protected]
List of Authors XIX
Tania IglesiasUniversity of OviedoDep. Statistics and O.R.3360 Oviedo, Spaine-mail: [email protected]
Giacomo IndiveriUniversity of Zurich and ETH ZurichInst. NeuroinformaticsZurich, Switzerlande-mail: [email protected]
Masahiro InuiguchiOsaka UniversityDep. Systems Innovation, Graduate School ofEngineering Science1-3 Machikaneyama-cho560-8531 Toyonaka, Osaka, Japane-mail: [email protected]
Hisao IshibuchiOsaka Prefecture UniversityDep. Computer Science and Intelligent Systems,Graduate School of Engineering1-1 Gakuen-Cho, Sakai599-8531 Osaka, Japane-mail: [email protected]
Emiliano IulianoCIRA, Italian Aerospace Research CenterFluid Dynamics Lab.Via Maiorise81043 Capua (CE), Italye-mail: [email protected]
Julie JacquesAlicante LAB50, rue Philippe de Girard59113 Seclin, Francee-mail: [email protected]
Andrzej JankowskiKnowledge Technology FoundationNowogrodzka 3100-511 Warsaw, Polande-mail: [email protected]
Balasubramaniam JayaramIndian Institute of Technology HyderabadDep. MathematicsODF Estate, Yeddumailaram502 205 Hyderabad, Indiae-mail: [email protected]
Laetitia JourdanUniversity of Lille 1INRIA/UFR IEEA/laboratory CRIStAL/CNRS59655 Lille, Francee-mail: [email protected]
Nikola KasabovAuckland University of TechnologyKEDRI – Knowledge Engineering andDiscovery Research Inst.120 Mayoral DriveAuckland, New Zealande-mail: [email protected]
Petra KerstingTU Dortmund UniversityDep. Mechanical EngineeringBaroper Str. 30344227 Dortmund, Germanye-mail: [email protected]
Erich P. KlementJohannes Kepler UniversityDep. Knowledge-Based Mathematical SystemsAltenberger Strasse 694040 Linz, Austriae-mail: [email protected]
Anna KolesárováSlovak University of Technology in BratislavaFaculty of Chemical and Food TechnologyRadlinského 9812 37 Bratislava, Slovakiae-mail: [email protected]
Magda KomorníkováSlovak University of TechnologyDep. MathematicsRadlinského 11813 68 Bratislava, Slovakiae-mail: [email protected]
Mark KotanchekEvolved Analytics LLC3411 Valley DriveMidland, MI 48640, USAe-mail: [email protected]
Robert KozmaUniversity of MemphisDep. Mathematical SciencesMemphis, TN 38152, USAe-mail: [email protected]
XX List of Authors
Tomáš KroupaInstitute of Information Theory and AutomationDep. Decision-Making TheoryPod Vodárenskou věží 4182 08 Prague, Czech Republice-mail: [email protected]
Rudolf KruseUniversity of MagdeburgFaculty of Computer ScienceUniversitätsplatz 239114 Magdeburg, Germanye-mail: [email protected]
Tufan KumbasarIstanbul Technical UniversityControl Engineering Dep.34469 Maslak, Istanbul, Turkeye-mail: [email protected]
James T. KwokHong Kong University of Science and TechnologyDep. Computer Science and EngineeringClear Water BayHong Kong, Hong Konge-mail: [email protected]
Rhyd LewisCardiff UniversitySchool of MathematicsCardiff, CF10 4AG, UKe-mail: [email protected]
Xiaodong LiRMIT UniversitySchool of Computer Science andInformation TechnologyMelbourne, 3001, Australiae-mail: [email protected]
Paulo J.G. LisboaLiverpool John Moores UniversityDep. Mathematics & StatisticsByrom StLiverpool, L3 3AF, UKe-mail: [email protected]
Weifeng LiuJump Trading600 W. Chicago Ave.Chicago, IL 60654, USAe-mail: [email protected]
Fernando G. LoboUniversidade do AlgarveDep. Engenharia Electrónica e InformáticaCampus de Gambelas8005-139 Faro, Portugale-mail: [email protected]
Antonio López JaimesCINVESTAV-IPNDep. ComputaciónAv. Instituto Politécnico Nacional No. 2508, Col.San Pedro ZacatencoD.F. 07300, México, Mexicoe-mail: [email protected]
Francisco LunaCentro Universitario de MéridaSanta Teresa de Jornet 3806800 Mérida, Spaine-mail: [email protected]
Luis MagdalenaEuropean Centre for Soft ComputingGonzalo Gutiérrez Quirós s/n33600 Mieres, Spaine-mail: [email protected]
Sebastia MassanetUniversity of the Balearic IslandsDep. Mathematics and Computer ScienceCrta. Valldemossa km. 7,507122 Palma de Mallorca, Spaine-mail: [email protected]
Benedetto MatarazzoUniversity of CataniaDep. Economics and BusinessCorso Italia 5595129 Catania, Italye-mail: [email protected]
Sergi Mateo BellidoPolytechnic University of CataloniaDep. Computer Architecture08034 Barcelona, Spaine-mail: [email protected]
James McDermottUniversity College DublinLochlann Quinn School of BusinessBelfieldDublin 4, Irelande-mail: [email protected]
List of Authors XXI
Patricia MelinTijuana Institute of TechnologyDep. Computer ScienceChula Vista, CA 91909, USAe-mail: [email protected]
Corrado MencarUniversity of BariDep. Informaticsvia E. Orabona, 470125 Bari, Italye-mail: [email protected]
Radko MesiarSTU in BratislavaDep. Mathematics and Descriptive GeometryRadlinskeho 11813 68 Bratislava, Slovakiae-mail: [email protected]
Ralf MikutKarlsruhe Institute of Technology (KIT)Inst. Applied Computer ScienceHermann-von-Helmholtz-Platz 176344 Eggenstein-Leopoldshafen, Germanye-mail: [email protected]
Ali A. MinaiUniversity of CincinnatiSchool of Electronic & Computing Systems2600 Clifton Ave.Cincinnati, OH 45221-0030, USAe-mail: [email protected]
Sadaaki MiyamotoUniversity of TsukubaRisk Engineering1-1-1 Tennodai305-8573 Tsukuba, Japane-mail: [email protected]
Christian MoewesUniversity of MagdeburgFaculty of Computer ScienceUniversitätsplatz 239114 Magdeburg, Germanye-mail: [email protected]
Javier MonteroComplutense University, MadridDep. Statistics and Operational ResearchPlaza de las Ciências, 328040 Madrid, Spaine-mail: [email protected]
Ignacio MontesUniversity of OviedoDep. Statistics and O.R.3360 Oviedo, Spaine-mail: [email protected]
Susana MontesUniversity of OviedoDep. Statistics and O.R.3360 Oviedo, Spaine-mail: [email protected]
Oscar H. Montiel RossAv. del Parque No. 131ºB.C. 22414, Mesa de Otay, Tijuana, Mexicoe-mail: [email protected]
Manuel MucientesUniversity of Santiago de CompostelaResearch Centre for Information Technologies15782 Santiago de Compostela, Spaine-mail: [email protected]
Nysret MusliuVienna University of TechnologyInst. Information SystemsFavoritenstraße 91000 Vienna, Austriae-mail: [email protected]
Yusuke NojimaOsaka Prefecture UniversityDep. Computer Science and Intelligent Systems,Graduate School of Engineering1-1 Gakuen-Cho, Sakai599-8531 Osaka, Japane-mail: [email protected]
Stefano NolfiConsiglio Nazionale delle Ricerche (CNR-ISTC)Inst. Cognitive Sciences and TechnologiesVia S. Martino della Battaglia, 4400185 Roma, Italye-mail: [email protected]
XXII List of Authors
Una-May O’ReillyMassachusetts Institute of TechnologyComputer Science and Artificial Intelligence Lab.32 Vassar St.Cambridge, MA 02139, USAe-mail: [email protected]
Miguel PagolaUniversidad Pública de NavarraDep. Automática y Computación31006 Pamplona (Navarra), Spaine-mail: [email protected]
Lynne ParkerUniversity of TennesseeDep. Electrical Engineering and Computer Science1520 Middle DriveKnoxville, TN 37996, USAe-mail: [email protected]
Kevin M. PassinoThe Ohio State UniversityDep. Electrical and Computer Engineering2015 Neil AvenueColumbus, OH 43210-1272, USAe-mail: [email protected]
Martin Pelikan1271 Lakeside Dr. #3123Sunnyvale, CA 94085, USAe-mail: [email protected]
Irina PerfilievaUniversity of OstravaInst. Research and Applications of Fuzzy Modeling30. dubna 2270103 Ostrava, Czech Republice-mail: [email protected]
Henry PradeUniversité Paul SabatierIRIT – Equipe ADRIA118 route de Narbonne31062 Toulouse Cedex 9, Francee-mail: [email protected]
Mike PreussWWU MünsterInst. WirtschaftsinformatikLeonardo-Campus 348149 Münster, Germanye-mail: [email protected]
José C. PrincipeUniversity of FloridaDep. Electrical and Computer EngineeringGainesville, FL 32611, USAe-mail: [email protected]
Domenico QuagliarellaCIRA, Italian Aerospace Research CenterFluid Dynamics Lab.Via Maiorise81043 Capua (CE), Italye-mail: [email protected]
Nicanor QuijanoUniversidad de los AndesDep. Electrical and Electronics EngineeringCra 1Este # 19A-40111711 Bogotá, Colombiae-mail: [email protected]
Jaroslav RamíkSilesian University in OpavaDep. Informatics and MathematicsUniversity Sq. 1934/373340 Karviná, Czech Republice-mail: [email protected]
Ismael Rodríguez FdezUniversity of Santiago de CompostelaResearch Centre for Information Technologies15782 Santiago de Compostela, Spaine-mail: [email protected]
Franz RothlaufJohannes Gutenberg University MainzGutenberg School of Management and EconomicsJakob Welder-Weg 955099 Mainz, Germanye-mail: [email protected]
Jonathan E. RoweUniversity of BirminghamSchool of Computer ScienceBirmingham, B15 2TT, UKe-mail: [email protected]
Imre J. RudasÓbuda UniversityDep. Applied MathematicsBécsi út 96/b1034 Budapest, Hungarye-mail: [email protected]
List of Authors XXIII
Günter RudolphTechnische Universität DortmundFak. InformatikOtto-Hahn-Str. 1444227 Dortmund, Germanye-mail: [email protected]
Gabriele SadowskiTechnische Universität DortmundBio- und ChemieingenieurwesenEmil-Figge-Str. 7044227 Dortmund, Germanye-mail: [email protected]
Marco ScarpaUniversita’ di MessinaDip. Ingegneria Civile, InformaticaContrada di Dio – S. Agata98166 Messina, Italye-mail: [email protected]
Werner SchafhauserXIMESHollandstraße 12/121020 Vienna, Austriae-mail: [email protected]
Roberto Sepúlveda CruzAv. del Parque No. 131ºB.C. 22414, Mesa de Otay, Tijuana, Mexicoe-mail: [email protected]
Jennie SiArizona State UniversitySchool of Electrical, Computer andEnergy EngineeringTempe, AZ 85287-5706, USAe-mail: [email protected]
Marco SignorettoKatholieke Universiteit LeuvenKasteelpark Arenberg 103001 Leuven, Belgiume-mail: [email protected]
Andrzej SkowronUniversity of WarsawFaculty of Mathematics,Computer Science and MechanicsBanacha 202-097 Warsaw, Polande-mail: [email protected]
Igor ŠkrjancUniversity of LjubljanaFaculty of Electrical EngineeringTržaška 251000 Ljubljana, Sloveniae-mail: [email protected]
Roman SłowińskiPoznań University of TechnologyInst. Computing SciencePiotrowo 260-965 Poznań, Polande-mail: [email protected]
Guido SmitsDow Benelux BVCore R&DHerbert H. Dowweg 54542 NM Hoek, The Netherlandse-mail: [email protected]
Ronen SosnikHolon Institute of Technology (H.I.T.)Electrical, Electronics and CommunicationEngineering52 Golomb St.5810201 Holon, Israele-mail: [email protected]
Alessandro SperdutiUniversity of PadovaDep. Pure and Applied MathematicsVia Trieste, 63351 21 Padova, Italye-mail: [email protected]
Kasper StøyIT University of CopenhagenRued Langgaards Vej 72300 Copenhagen S, Denmarke-mail: [email protected]
Harrison StrattonArizona State University & BarrowNeurological InstitutePhoenix, AZ 85013, USAe-mail: [email protected]
XXIV List of Authors
Thomas StützleUniversité libre de Bruxelles (ULB)IIRIDIA, CP 194/6Av. F. Roosevelt 501050 Brussels, Belgiume-mail: [email protected]
Dirk SudholtUniversity of SheffieldDep. Computer Science211 PortobelloSheffield, S1 4DP, UKe-mail: [email protected]
Ron SunRensselaer Polytechnic InstituteCognitive Science Dep.110 Eighth Street, Carnegie 302ATroy, NY 12180, USAe-mail: [email protected]
Johan A. K. SuykensKatholieke Universiteit LeuvenKasteelpark Arenberg 103001 Leuven, Belgiume-mail: [email protected]
Roman W. Swiniarski (deceased)
El-Ghazali TalbiUniversity of LilleComputer Science CRISTALBat.M3 cité scientifique59655 Villeneuve d’Ascq, Francee-mail: [email protected]
Lothar ThieleSwiss Federal Institute of Technology ZurichComputer Engineering and Networks Lab.Gloriastrasse 358092 Zurich, Switzerlande-mail: [email protected]
Peter TinoUniversity of BirminghamSchool of Computer ScienceEdgbastonBirmingham, B15 2TT, UKe-mail: [email protected]
Joan TorrensUniversity of the Balearic IslandsDep. Mathematics and Computer ScienceCrta. Valldemossa km. 7,507122 Palma de Mallorca, Spaine-mail: [email protected]
Vito TrianniConsiglio Nazionale delle RicercheIst. Scienze e Tecnologie della Cognizionevia San Martino della Battaglia 4400185 Roma, Italye-mail: [email protected]
Enric TrillasEuropean Centre for Soft ComputingFundamentals of Soft Computing33600 Mieres, Spaine-mail: [email protected]
Fevrier ValdezTijuana Institute of TechnologyCalzada del Tecnológico S/N, Tomas AquinoB.C. 22414, Tijuana, Mexicoe-mail: [email protected]
Nele VerbiestGhent UniversityDep. Applied Mathematics,Computer Science and StatisticsKrijgslaan 281 (S9)9000 Ghent, Belgiume-mail: [email protected]
Thomas VillmannUniversity of Applied Sciences MittweidaDep. Mathematics, Natural and ComputerSciencesTechnikumplatz 1709648 Mittweida, Germanye-mail: [email protected]
Milan VlachCharles UniversityTheoretical Computer Science andMathematical LogicMalostranské náměstí 25118 00 Prague, Czech Republice-mail: [email protected]
List of Authors XXV
Ekaterina VladislavlevaEvolved Analytics Europe BVBAA. Coppenslaan 272300 Turnhout, Belgiume-mail: [email protected]
Tobias WagnerTU Dortmund UniversityDep. Mechanical EngineeringBaroper Str. 30344227 Dortmund, Germanye-mail: [email protected]
Jun WangThe Chinese University of Hong KongDep. Mechanical & Automation EngineeringShatin, New TerritoriesHongkong, Hong Konge-mail: [email protected]
Simon WessingTechnische Universität DortmundFak. InformatikOtto-Hahn-Str. 1444227 Dortmund, Germanye-mail: [email protected]
Wei-Zhi WuZhejiang Ocean UniversitySchool of Mathematics, Physics andInformation ScienceNo.1 Haida South Road, Lincheng District316022 Zhoushan, Zhejiang, Chinae-mail: [email protected]
Lei XuThe Chinese University of Hong KongDep. Computer Science and EngineeringShatin, New TerritoriesHong Kong, Hong Konge-mail: [email protected]
JingTao YaoUniversity of ReginaDep. Computer Science3737 Wascana ParkwayRegina, Saskatchewan, S4S 0A2, Canadae-mail: [email protected]
Yiyu YaoUniversity of ReginaDep. Computer Science3737 Wascana ParkwayRegina, Saskatchewan, S4S 0A2, Canadae-mail: [email protected]
Andreas ZabelTU Dortmund UniversityDep. Mechanical EngineeringBaroper Str. 30344227 Dortmund, Germanye-mail: [email protected]
Sławomir ZadrożnyPolish Academy of SciencesSystems Research Inst.ul. Newelska 601-447 Warsaw, Polande-mail: [email protected]
Zhigang ZengHuazhong University of Science and TechnologyDep. Control Science and EngineeringNo. 1037, Luoyu Road430074 Wuhan, Chinae-mail: [email protected]
Yan ZhangUniversity of ReginaDep. Computer Science3737 Wascana ParkwayRegina, Saskatchewan, S4S 0A2, Canadae-mail: [email protected]
Zhi-Hua ZhouNanjing UniversityNational Key Lab. for Novel Software Technology210023 Nanjing, Chinae-mail: [email protected]
XXVII
Contents
List of Abbreviations ............................................................. XLV
1 IntroductionJanusz Kacprzyk, Witold Pedrycz ............................................... 11.1 Details of the Contents.................................................. 21.2 Conclusions and Acknowledgments ................................... 4
Part A Foundations
2 Many-Valued and Fuzzy LogicsSiegfried Gottwald .............................................................. 72.1 Basic Many-Valued Logics .............................................. 82.2 Fuzzy Sets ................................................................ 112.3 t-Norm-Based Logics ................................................... 132.4 Particular Fuzzy Logics .................................................. 162.5 Some Generalizations ................................................... 212.6 Extensions with Graded Notions of Inference ........................ 232.7 Some Complexity Results ............................................... 252.8 Concluding Remarks .................................................... 27References ....................................................................... 27
3 Possibility Theory and Its Applications: Where Do We Stand?Didier Dubois, Henry Prade ..................................................... 313.1 Historical Background................................................... 323.2 Basic Notions of Possibility Theory..................................... 333.3 Qualitative Possibility Theory........................................... 383.4 Quantitative Possibility Theory ......................................... 453.5 Some Applications....................................................... 493.6 Some Current Research Lines ........................................... 53References ....................................................................... 54
4 Aggregation Functions on [0,1]Radko Mesiar, Anna Kolesárová, Magda Komorníková ...................... 614.1 Historical and Introductory Remarks .................................. 614.2 Classification of Aggregation Functions................................ 634.3 Properties and Construction Methods ................................. 664.4 Concluding Remarks .................................................... 71References ....................................................................... 72
5 Monotone Measures-Based IntegralsErich P. Klement, Radko Mesiar ................................................ 755.1 Preliminaries, Choquet, and Sugeno Integrals ........................ 765.2 Benvenuti Integral ...................................................... 80
XXVIII Contents
5.3 Universal Integrals ...................................................... 825.4 General Integrals Which Are Not Universal ............................ 845.5 Concluding Remarks, Application Fields............................... 86References ....................................................................... 87
6 The Origin of Fuzzy ExtensionsHumberto Bustince, Edurne Barrenechea, Javier Fernández,Miguel Pagola, Javier Montero ................................................. 896.1 Considerations Prior to the Concept of Extension of Fuzzy Sets ..... 906.2 Origin of the Extensions ................................................ 936.3 Type-2 Fuzzy Sets ....................................................... 946.4 Interval-Valued Fuzzy Sets ............................................. 986.5 Atanasssov’s Intuitionistic Fuzzy Sets or Bipolar Fuzzy Sets
of Type 2 or IF Fuzzy Sets ............................................... 1036.6 Atanassov’s Interval-Valued Intuitionistic Fuzzy Sets ................ 1056.7 Links Between the Extensions of Fuzzy Sets .......................... 1066.8 Other Types of Sets ...................................................... 1066.9 Conclusions .............................................................. 108References ....................................................................... 108
7 F-TransformIrina Perfilieva ................................................................... 1137.1 Fuzzy Modeling .......................................................... 1137.2 Fuzzy Partitions.......................................................... 1147.3 Fuzzy Transform ......................................................... 1177.4 Discrete F-Transform .................................................... 1197.5 F-Transforms of Functions of Two Variables .......................... 1207.6 F1-Transform............................................................. 1217.7 Applications.............................................................. 1227.8 Conclusions .............................................................. 129References ....................................................................... 129
8 Fuzzy Linear Programming and DualityJaroslav Ramík, Milan Vlach.................................................... 1318.1 Preliminaries............................................................. 1328.2 Fuzzy Linear Programming.............................................. 1358.3 Duality in Fuzzy Linear Programming.................................. 1378.4 Conclusion ............................................................... 143References ....................................................................... 143
9 Basic Solutions of Fuzzy Coalitional GamesTomáš Kroupa, Milan Vlach..................................................... 1459.1 Coalitional Games with Transferable Utility ........................... 1469.2 Coalitional Games with Fuzzy Coalitions .............................. 1509.3 Final Remarks............................................................ 155References ....................................................................... 156
Contents XXIX
Part B Fuzzy Logic
10 Basics of Fuzzy SetsJános C. Fodor, Imre J. Rudas .................................................. 15910.1 Classical Mathematics and Logic ....................................... 16010.2 Fuzzy Logic, Membership Functions, and Fuzzy Sets ................. 16010.3 Connectives in Fuzzy Logic.............................................. 16110.4 Concluding Remarks .................................................... 168References ....................................................................... 168
11 Fuzzy Relations: Past, Present, and FutureSusana Montes, Ignacio Montes, Tania Iglesias .............................. 17111.1 Fuzzy Relations .......................................................... 17211.2 Cut Relations............................................................. 17411.3 Fuzzy Binary Relations .................................................. 17411.4 Particular Cases of Fuzzy Binary Relations............................. 17911.5 Present and Future of Fuzzy Relations................................. 180References ....................................................................... 180
12 Fuzzy Implications: Past, Present, and FutureMichał Baczynski, Balasubramaniam Jayaram, Sebastia Massanet,Joan Torrens ..................................................................... 18312.1 Fuzzy Implications: Examples, Properties, and Classes .............. 18412.2 Current Research on Fuzzy Implications ............................... 18712.3 Fuzzy Implications in Applications..................................... 19312.4 Future of Fuzzy Implications ........................................... 198References ....................................................................... 199
13 Fuzzy Rule-Based SystemsLuis Magdalena.................................................................. 20313.1 Components of a Fuzzy Rule Based-System........................... 20413.2 Types of Fuzzy Rule-Based Systems .................................... 20913.3 Hierarchical Fuzzy Rule-Based Systems................................ 21313.4 Fuzzy Rule-Based Systems Design ..................................... 21413.5 Conclusions .............................................................. 216References ....................................................................... 217
14 Interpretability of Fuzzy Systems:Current Research Trends and ProspectsJose M. Alonso, Ciro Castiello, Corrado Mencar ................................ 21914.1 The Quest for Interpretability........................................... 22014.2 Interpretability Constraints and Criteria ............................... 22414.3 Interpretability Assessment ............................................ 22714.4 Designing Interpretable Fuzzy Systems ................................ 22914.5 Interpretable Fuzzy Systems in the Real World ....................... 23314.6 Future Research Trends on Interpretable Fuzzy Systems ............. 23414.7 Conclusions .............................................................. 234References ....................................................................... 235
XXX Contents
15 Fuzzy Clustering – Basic Ideas and OverviewSadaaki Miyamoto .............................................................. 23915.1 Fuzzy Clustering ......................................................... 23915.2 Fuzzy c-Means........................................................... 23915.3 Hierarchical Fuzzy Clustering ........................................... 24515.4 Conclusion ............................................................... 246References ....................................................................... 247
16 An Algebraic Model of Reasoning to Support Zadeh’s CWWEnric Trillas ....................................................................... 24916.1 A View on Reasoning .................................................... 24916.2 Models ................................................................... 25016.3 Reasoning ................................................................ 25116.4 Reasoning and Logic .................................................... 25416.5 A Possible Scheme for an Algebraic Model
of Commonsense Reasoning............................................ 25516.6 Weak and Strong Deduction: Refutations and Conjectures
in a BFA (with a Few Restrictions) ..................................... 26016.7 Toward a Classification of Conjectures ................................. 26216.8 Last Remarks............................................................. 26416.9 Conclusions .............................................................. 265References ....................................................................... 266
17 Fuzzy ControlChristian Moewes, Ralf Mikut, Rudolf Kruse................................... 26917.1 Knowledge-Driven Control ............................................. 26917.2 Classical Control Engineering ........................................... 27017.3 Using Fuzzy Rules for Control ........................................... 27117.4 A Glance at Some Industrial Applications ............................. 27617.5 Automatic Learning of Fuzzy Controllers............................... 27917.6 Conclusions .............................................................. 281References ....................................................................... 281
18 Interval Type-2 Fuzzy PID ControllersTufan Kumbasar, Hani Hagras.................................................. 28518.1 Fuzzy Control Background .............................................. 28518.2 The General Fuzzy PID Controller Structure ............................ 28618.3 Simulation Studies ...................................................... 29118.4 Conclusion ............................................................... 292References ....................................................................... 293
19 Soft Computing in Database and Information ManagementGuy De Tré, Sławomir Zadrożny................................................. 29519.1 Challenges for Modern Information Systems .......................... 29519.2 Some Preliminaries...................................................... 29619.3 Soft Computing in Information Modeling ............................. 29819.4 Soft Computing in Querying ............................................ 30219.5 Conclusions .............................................................. 309References ....................................................................... 309
Contents XXXI
20 Application of Fuzzy Techniques to Autonomous RobotsIsmael Rodríguez Fdez, Manuel Mucientes, Alberto Bugarín Diz ............ 31320.1 Robotics and Fuzzy Logic ............................................... 31320.2 Wall-Following .......................................................... 31420.3 Navigation ............................................................... 31520.4 Trajectory Tracking....................................................... 31720.5 Moving Target Tracking .................................................. 31820.6 Perception ............................................................... 31920.7 Planning ................................................................. 31920.8 SLAM ...................................................................... 32020.9 Cooperation .............................................................. 32020.10 Legged Robots ........................................................... 32120.11 Exoskeletons and Rehabilitation Robots .............................. 32220.12 Emotional Robots ....................................................... 32320.13 Fuzzy Modeling .......................................................... 32320.14 Comments and Conclusions ............................................ 324References ....................................................................... 325
Part C Rough Sets
21 Foundations of Rough SetsAndrzej Skowron, Andrzej Jankowski, Roman W. Swiniarski ................ 33121.1 Rough Sets: Comments on Development .............................. 33121.2 Vague Concepts .......................................................... 33221.3 Rough Set Philosophy ................................................... 33321.4 Indiscernibility and Approximation.................................... 33321.5 Decision Systems and Decision Rules .................................. 33621.6 Dependencies............................................................ 33721.7 Reduction of Attributes ................................................. 33721.8 Rough Membership ..................................................... 33821.9 Discernibility and Boolean Reasoning ................................. 33921.10 Rough Sets and Induction .............................................. 34021.11 Rough Set-Based Generalizations ..................................... 34021.12 Rough Sets and Logic ................................................... 34321.13 Conclusions .............................................................. 347References ....................................................................... 347
22 Rough Set Methodology for Decision AidingRoman Słowiński, Salvatore Greco, Benedetto Matarazzo ................... 34922.1 Data Inconsistency as a Reason for Using Rough Sets ................ 35022.2 The Need for Replacing the Indiscernibility Relation
by the Dominance Relation when Reasoning About Ordinal Data .. 35122.3 The Dominance-based Rough Set Approach
to Multi-Criteria Classification.......................................... 35322.4 The Dominance-based Rough Set Approach to Multi-Criteria
Choice and Ranking ..................................................... 36122.5 Important Extensions of DRSA .......................................... 366
XXXII Contents
22.6 DRSA to Operational Research Problems ............................... 36622.7 Concluding Remarks on DRSA Applied to Multi-Criteria
Decision Problems....................................................... 367References ....................................................................... 367
23 Rule Induction from Rough ApproximationsJerzy W. Grzymala-Busse ....................................................... 37123.1 Complete and Consistent Data ......................................... 37123.2 Inconsistent Data........................................................ 37523.3 Decision Table with Numerical Attributes ............................. 37723.4 Incomplete Data......................................................... 37823.5 Conclusions .............................................................. 384References ....................................................................... 384
24 Probabilistic Rough SetsYiyu Yao, Salvatore Greco, Roman Słowiński .................................. 38724.1 Motivation for Studying Probabilistic Rough Sets..................... 38824.2 Pawlak Rough Sets ...................................................... 38824.3 A Basic Model of Probabilistic Rough Sets ............................. 39024.4 Variants of Probabilistic Rough Sets ................................... 39124.5 Three Fundamental Issues of Probabilistic Rough Sets ............... 39424.6 Dominance-Based Rough Set Approaches ............................ 39824.7 A Basic Model of Dominance-Based Probabilistic Rough Sets ....... 39924.8 Variants of Probabilistic Dominance-Based Rough Set Approach ... 40024.9 Three Fundamental Issues of Probabilistic Dominance-Based
Rough Sets ............................................................... 40324.10 Conclusions .............................................................. 409References ....................................................................... 409
25 Generalized Rough SetsJingTao Yao, Davide Ciucci, Yan Zhang ........................................ 41325.1 Definition and Approximations of the Models ........................ 41425.2 Theoretical Approaches ................................................. 42025.3 Conclusion ............................................................... 422References ....................................................................... 423
26 Fuzzy-Rough HybridizationMasahiro Inuiguchi, Wei-Zhi Wu, Chris Cornelis, Nele Verbiest .............. 42526.1 Introduction to Fuzzy-Rough Hybridization .......................... 42526.2 Classification- Versus Approximation-Oriented
Fuzzy Rough Set Models ................................................ 42726.3 Generalized Fuzzy Belief Structures with Application
in Fuzzy Information Systems .......................................... 43726.4 Applications of Fuzzy Rough Sets ...................................... 444References ....................................................................... 447
Contents XXXIII
Part D Neural Networks
27 Artificial Neural Network ModelsPeter Tino, Lubica Benuskova, Alessandro Sperduti .......................... 45527.1 Biological Neurons ...................................................... 45527.2 Perceptron ............................................................... 45627.3 Multilayered Feed-Forward ANN Models .............................. 45827.4 Recurrent ANN Models .................................................. 46027.5 Radial Basis Function ANN Models ..................................... 46427.6 Self-Organizing Maps ................................................... 46527.7 Recursive Neural Networks ............................................. 46727.8 Conclusion ............................................................... 469References ....................................................................... 470
28 Deep and Modular Neural NetworksKe Chen........................................................................... 47328.1 Overview ................................................................. 47328.2 Deep Neural Networks .................................................. 47428.3 Modular Neural Networks............................................... 48428.4 Concluding Remarks .................................................... 492References ....................................................................... 492
29 Machine LearningJames T. Kwok, Zhi-Hua Zhou, Lei Xu ......................................... 49529.1 Overview ................................................................. 49529.2 Supervised Learning..................................................... 49729.3 Unsupervised Learning.................................................. 50229.4 Reinforcement Learning ................................................ 51029.5 Semi-Supervised Learning.............................................. 51329.6 Ensemble Methods ...................................................... 51429.7 Feature Selection and Extraction....................................... 518References ....................................................................... 519
30 Theoretical Methods in Machine LearningBadong Chen, Weifeng Liu, José C. Principe................................... 52330.1 Background Overview ................................................... 52430.2 Reproducing Kernel Hilbert Spaces .................................... 52530.3 Online Learning with Kernel Adaptive Filters ......................... 52730.4 Illustration Examples.................................................... 53830.5 Conclusion ............................................................... 542References ....................................................................... 542
31 Probabilistic Modeling in Machine LearningDavide Bacciu, Paulo J.G. Lisboa, Alessandro Sperduti, Thomas Villmann . 54531.1 Probabilistic and Information-Theoretic Methods.................... 54531.2 Graphical Models ........................................................ 55231.3 Latent Variable Models.................................................. 56031.4 Markov Models .......................................................... 56531.5 Conclusion and Further Reading ....................................... 572References ....................................................................... 573
XXXIV Contents
32 Kernel MethodsMarco Signoretto, Johan A. K. Suykens ........................................ 57732.1 Background .............................................................. 57832.2 Foundations of Statistical Learning .................................... 58032.3 Primal–Dual Methods ................................................... 58632.4 Gaussian Processes ...................................................... 59332.5 Model Selection ......................................................... 59632.6 More on Kernels ......................................................... 59732.7 Applications.............................................................. 600References ....................................................................... 601
33 NeurodynamicsRobert Kozma, Jun Wang, Zhigang Zeng...................................... 60733.1 Dynamics of Attractor and Analog Networks .......................... 60733.2 Synchrony, Oscillations, and Chaos in Neural Networks .............. 61133.3 Memristive Neurodynamics............................................. 62933.4 Neurodynamic Optimization............................................ 634References ....................................................................... 639
34 Computational Neuroscience – Biophysical Modelingof Neural SystemsHarrison Stratton, Jennie Si..................................................... 64934.1 Anatomy and Physiology of the Nervous System ..................... 64934.2 Cells and Signaling Among Cells........................................ 65234.3 Modeling Biophysically Realistic Neurons ............................. 65634.4 Reducing Computational Complexity
for Large Network Simulations ......................................... 66034.5 Conclusions .............................................................. 662References ....................................................................... 662
35 Computational Models of Cognitive and Motor ControlAli A. Minai ...................................................................... 66535.1 Overview ................................................................. 66535.2 Motor Control ............................................................ 66735.3 Cognitive Control and Working Memory ............................... 67035.4 Conclusion ............................................................... 674References ....................................................................... 674
36 Cognitive Architectures and AgentsSebastien Hélie, Ron Sun ....................................................... 68336.1 Background .............................................................. 68336.2 Adaptive Control of Thought-Rational (ACT-R) ........................ 68536.3 Soar....................................................................... 68836.4 CLARION................................................................... 69036.5 Cognitive Architectures as Models of Multi-Agent Interaction ....... 69336.6 General Discussion ...................................................... 694References ....................................................................... 695
Contents XXXV
37 Embodied IntelligenceAngelo Cangelosi, Josh Bongard, Martin H. Fischer, Stefano Nolfi .......... 69737.1 Introduction to Embodied Intelligence................................ 69737.2 Morphological Computation for Body-Behavior Coadaptation ...... 69837.3 Sensory–Motor Coordination in Evolving Robots ..................... 70137.4 Developmental Robotics for Higher Order Embodied Cognitive
Capabilities .............................................................. 70337.5 Conclusion ............................................................... 709References ....................................................................... 711
38 Neuromorphic EngineeringGiacomo Indiveri ................................................................ 71538.1 The Origins ............................................................... 71538.2 Neural and Neuromorphic Computing ................................. 71638.3 The Importance of Fundamental Neuroscience ....................... 71738.4 Temporal Dynamics in Neuromorphic Architectures .................. 71838.5 Synapse and Neuron Circuits ........................................... 71938.6 Spike-Based Multichip Neuromorphic Systems ....................... 72138.7 State-Dependent Computation in Neuromorphic Systems........... 72238.8 Conclusions .............................................................. 722References ....................................................................... 723
39 NeuroengineeringDamien Coyle, Ronen Sosnik ................................................... 72739.1 Overview – Neuroengineering in General ............................. 72839.2 Human Motor Control ................................................... 73239.3 Modeling the Motor System – Internal Motor Models ................ 73339.4 Sensorimotor Learning .................................................. 73639.5 MRI and the Motor System – Structure and Function ................ 73839.6 Electrocorticographic Motor Cortical Surface Potentials .............. 74139.7 MEG and EEG – Extra Cerebral Magnetic and Electric Fields
of the Motor System..................................................... 74539.8 Extracellular Recording – Decoding Hand Movements
from Spikes and Local Field Potential ................................. 74839.9 Translating Brainwaves into Control Signals – BCIs ................... 75439.10 Conclusion ............................................................... 762References ....................................................................... 764
40 Evolving Connectionist Systems:From Neuro-Fuzzy-, to Spiking- and Neuro-GeneticNikola Kasabov .................................................................. 77140.1 Principles of Evolving Connectionist Systems (ECOS) .................. 77140.2 Hybrid Systems and Evolving Neuro-Fuzzy Systems .................. 77240.3 Evolving Spiking Neural Networks (eSNN) ............................. 77540.4 Computational Neuro-Genetic Modeling (CNGM) ..................... 77840.5 Conclusions and Further Directions .................................... 779References ....................................................................... 780
XXXVI Contents
41 Machine Learning ApplicationsPiero P. Bonissone ............................................................... 78341.1 Motivation ............................................................... 78441.2 Machine Learning (ML) Functions ...................................... 78641.3 CI/ML Applications in Industrial Domains:
Prognostics and Health Management (PHM) .......................... 78741.4 CI/ML Applications in Financial Domains: Risk Management ........ 79741.5 Model Ensembles and Fusion .......................................... 80741.6 Summary and Future Research Challenges ............................ 812References ....................................................................... 817
Part E Evolutionary Computation
42 Genetic AlgorithmsJonathan E. Rowe ............................................................... 82542.1 Algorithmic Framework ................................................. 82642.2 Selection Methods....................................................... 82842.3 Replacement Methods .................................................. 83142.4 Mutation Methods....................................................... 83242.5 Selection–Mutation Balance ........................................... 83442.6 Crossover Methods ...................................................... 83642.7 Population Diversity..................................................... 83842.8 Parallel Genetic Algorithms............................................. 83942.9 Populations as Solutions ............................................... 84142.10 Conclusions .............................................................. 842References ....................................................................... 843
43 Genetic ProgrammingJames McDermott, Una-May O’Reilly .......................................... 84543.1 Evolutionary Search for Executable Programs ......................... 84543.2 History.................................................................... 84643.3 Taxonomy of AI and GP ................................................. 84843.4 Uses of GP ................................................................ 85343.5 Research Topics .......................................................... 85743.6 Practicalities ............................................................. 861References ....................................................................... 862
44 Evolution StrategiesNikolaus Hansen, Dirk V. Arnold, Anne Auger................................. 87144.1 Overview ................................................................. 87144.2 Main Principles .......................................................... 87344.3 Parameter Control ....................................................... 87744.4 Theory .................................................................... 886References ....................................................................... 895
45 Estimation of Distribution AlgorithmsMartin Pelikan, Mark W. Hauschild, Fernando G. Lobo ...................... 89945.1 Basic EDA Procedure ..................................................... 900
Contents XXXVII
45.2 Taxonomy of EDA Models ............................................... 90345.3 Overview of EDAs ........................................................ 90845.4 EDA Theory ............................................................... 91645.5 Efficiency Enhancement Techniques for EDAs ......................... 91745.6 Starting Points for Obtaining Additional Information ................ 92045.7 Summary and Conclusions.............................................. 921References ....................................................................... 921
46 Parallel Evolutionary AlgorithmsDirk Sudholt...................................................................... 92946.1 Parallel Models .......................................................... 93146.2 Effects of Parallelization ................................................ 93546.3 On the Spread of Information in Parallel EAs ......................... 93846.4 Examples Where Parallel EAs Excel ..................................... 94346.5 Speedups by Parallelization ............................................ 94946.6 Conclusions .............................................................. 956References ....................................................................... 957
47 Learning Classifier SystemsMartin V. Butz ................................................................... 96147.1 Background .............................................................. 96247.2 XCS ........................................................................ 96547.3 XCSF ....................................................................... 97047.4 Data Mining.............................................................. 97247.5 Behavioral Learning ..................................................... 97347.6 Conclusions .............................................................. 97747.7 Books and Source Code ................................................. 978References ....................................................................... 979
48 Indicator-Based SelectionLothar Thiele ..................................................................... 98348.1 Motivation ............................................................... 98348.2 Basic Concepts ........................................................... 98448.3 Selection Schemes....................................................... 98748.4 Preference-Based Selection ............................................ 99048.5 Concluding Remarks .................................................... 992References ....................................................................... 993
49 Multi-Objective Evolutionary AlgorithmsKalyanmoy Deb .................................................................. 99549.1 Preamble ................................................................. 99549.2 Evolutionary Multi-Objective Optimization (EMO) .................... 99649.3 A Brief Timeline for the Development of EMO Methodologies ....... 99949.4 Elitist EMO: NSGA-II ..................................................... 100049.5 Applications of EMO ..................................................... 100249.6 Recent Developments in EMO .......................................... 100449.7 Conclusions .............................................................. 1010References ....................................................................... 1011
XXXVIII Contents
50 Parallel Multiobjective Evolutionary AlgorithmsFrancisco Luna, Enrique Alba................................................... 101750.1 Multiobjective Optimization and Parallelism ......................... 101750.2 Parallel Models for Evolutionary Multi-Objective Algorithms ........ 101850.3 An Updated Review of the Literature .................................. 102050.4 Conclusions and Future Works ......................................... 1026References ....................................................................... 1027
51 Many-Objective Problems: Challenges and MethodsAntonio López Jaimes, Carlos A. Coello Coello ................................. 103351.1 Background .............................................................. 103351.2 Basic Concepts and Notation ........................................... 103451.3 Sources of Difficulty to Solve Many-Objective
Optimization Problems.................................................. 103651.4 Current Approaches to Deal with Many-Objective Problems......... 103851.5 Recombination Operators and Mating Restrictions ................... 104251.6 Scalarization Methods .................................................. 104351.7 Conclusions and Research Paths ....................................... 1043References ....................................................................... 1044
52 Memetic and Hybrid Evolutionary AlgorithmsJhon Edgar Amaya, Carlos Cotta Porras, Antonio J. Fernández Leiva ....... 104752.1 Overview ................................................................. 104752.2 A Bird’s View of Evolutionary Algorithms.............................. 104952.3 From Hybrid Metaheuristics to Hybrid EAs ............................ 105052.4 Memetic Algorithms ..................................................... 105252.5 Cooperative Optimization Models ...................................... 105552.6 Conclusions .............................................................. 1056References ....................................................................... 1056
53 Design of Representations and Search OperatorsFranz Rothlauf ................................................................... 106153.1 Representations ......................................................... 106153.2 Search Operators......................................................... 106553.3 Problem-Specific Design of Representations and Search Operators. 107153.4 Summary and Conclusions.............................................. 1079References ....................................................................... 1080
54 Stochastic Local Search Algorithms: An OverviewHolger H. Hoos, Thomas Stützle ................................................ 108554.1 The Nature and Concept of SLS ......................................... 108654.2 Greedy Construction Heuristics and Iterative Improvement ......... 108954.3 Simple SLS Methods ..................................................... 109154.4 Hybrid SLS Methods ..................................................... 109454.5 Population-Based SLS Methods ........................................ 109554.6 Recent Research Directions ............................................. 1097References ....................................................................... 1100
Contents XXXIX
55 Parallel Evolutionary Combinatorial OptimizationEl-Ghazali Talbi ................................................................. 110755.1 Motivation ............................................................... 110755.2 Parallel Design of EAs ................................................... 110855.3 Parallel Implementation of EAs ........................................ 111355.4 Parallel EAs Under ParadisEO ........................................... 112255.5 Conclusions and Perspectives .......................................... 1123References ....................................................................... 1124
56 How to Create Generalizable ResultsThomas Bartz-Beielstein........................................................ 112756.1 Test Problems in Computational Intelligence ......................... 112756.2 Features of Optimization Problems .................................... 112856.3 Algorithm Features ...................................................... 113056.4 Objective Functions ..................................................... 113156.5 Case Studies.............................................................. 113356.6 Summary and Outlook .................................................. 1141References ....................................................................... 1142
57 Computational Intelligence in Industrial ApplicationsEkaterina Vladislavleva, Guido Smits, Mark Kotanchek ...................... 114357.1 Intelligence and Computation ......................................... 114357.2 Computational Modeling for Predictive Analytics..................... 114457.3 Methods.................................................................. 114757.4 Workflows................................................................ 114957.5 Examples ................................................................. 115057.6 Conclusions .............................................................. 1155References ....................................................................... 1156
58 Solving Phase Equilibrium Problemsby Means of Avoidance-Based MultiobjectivizationMike Preuss, Simon Wessing, Günter Rudolph, Gabriele Sadowski.......... 115958.1 Coping with Real-World Optimization Problems ..................... 115958.2 The Phase-Equilibrium Calculation Problem.......................... 116158.3 Multiobjectivization-Assisted Multimodal Optimization: MOAMO ... 116258.4 Solving General Phase-Equilibrium Problems ........................ 116558.5 Conclusions and Outlook ............................................... 1169References ....................................................................... 1169
59 Modeling and Optimization of Machining ProblemsDirk Biermann, Petra Kersting, Tobias Wagner, Andreas Zabel .............. 117359.1 Elements of a Machining Process ...................................... 117459.2 Design Optimization..................................................... 117559.3 Computer-Aided Design and Manufacturing.......................... 117659.4 Modeling and Simulation of the Machining Process ................. 117759.5 Optimization of the Process Parameters ............................... 117859.6 Process Monitoring ...................................................... 117959.7 Visualization ............................................................. 1179
XL Contents
59.8 Summary and Outlook .................................................. 1180References ....................................................................... 1180
60 Aerodynamic Design with Physics-Based SurrogatesEmiliano Iuliano, Domenico Quagliarella ..................................... 118560.1 The Aerodynamic Design Problem ..................................... 118660.2 Literature Review of Surrogate-Based Optimization.................. 118760.3 POD-Based Surrogates .................................................. 119060.4 Application Example of POD-Based Surrogates ....................... 119160.5 Strategies for Improving POD Model Quality: Adaptive Sampling.... 119960.6 Aerodynamic Shape Optimization by Surrogate Modeling
and Evolutionary Computing ........................................... 120160.7 Conclusions .............................................................. 1207References ....................................................................... 1208
61 Knowledge Discovery in BioinformaticsJulie Hamon, Julie Jacques, Laetitia Jourdan, Clarisse Dhaenens ........... 121161.1 Challenges in Bioinformatics ........................................... 121161.2 Association Rules by Evolutionary Algorithm in Bioinformatics ..... 121261.3 Feature Selection for Classification and Regression
by Evolutionary Algorithm in Bioinformatics.......................... 121561.4 Clustering by Evolutionary Algorithm in Bioinformatics.............. 121861.5 Conclusion ............................................................... 1220References ....................................................................... 1221
62 Integration of Metaheuristics and Constraint ProgrammingLuca Di Gaspero.................................................................. 122562.1 Constraint Programming and Metaheuristics ......................... 122562.2 Constraint Programming Essentials .................................... 122662.3 Integration of Metaheuristics and CP .................................. 123062.4 Conclusions .............................................................. 1234References ....................................................................... 1235
63 Graph Coloring and RecombinationRhyd Lewis ....................................................................... 123963.1 Graph Coloring........................................................... 123963.2 Algorithms for Graph Coloring.......................................... 124063.3 Setup ..................................................................... 124463.4 Experiment 1 ............................................................ 124663.5 Experiment 2 ............................................................ 124963.6 Conclusions and Discussion ............................................ 1251References ....................................................................... 1252
64 Metaheuristic Algorithms and Tree DecompositionThomas Hammerl, Nysret Musliu, Werner Schafhauser....................... 125564.1 Tree Decompositions .................................................... 125664.2 Generating Tree Decompositions by Metaheuristic Techniques...... 125864.3 Conclusion ............................................................... 1268References ....................................................................... 1269
Contents XLI
65 Evolutionary Computation and Constraint SatisfactionJano I. van Hemert .............................................................. 127165.1 Informal Introduction to CSP ........................................... 127165.2 Formal Definitions ...................................................... 127265.3 Solving CSP with Evolutionary Algorithms ............................. 127365.4 Performance Indicators ................................................. 127565.5 Specific Constraint Satisfaction Problems.............................. 127765.6 Creating Rather than Solving Problems................................ 128365.7 Conclusions and Future Directions ..................................... 1284References ....................................................................... 1284
Part F Swarm Intelligence
66 Swarm Intelligence in Optimization and RoboticsChristian Blum, Roderich Groß ................................................. 129166.1 Overview ................................................................. 129166.2 SI in Optimization ....................................................... 129266.3 SI in Robotics: Swarm Robotics......................................... 129666.4 Research Challenges..................................................... 1302References ....................................................................... 1303
67 Preference-Based MultiobjectiveParticle Swarm Optimization for Airfoil DesignRobert Carrese, Xiaodong Li .................................................... 131167.1 Airfoil Design ............................................................ 131167.2 Shape Parameterization and Flow Solver ............................. 131767.3 Optimization Algorithm ................................................. 131967.4 Case Study: Airfoil Shape Optimization ................................ 132367.5 Conclusion ............................................................... 1329References ....................................................................... 1329
68 Ant Colony Optimization for the Minimum-Weight RootedArborescence ProblemChristian Blum, Sergi Mateo Bellido ........................................... 133368.1 Introductiory Remarks .................................................. 133368.2 The Minimum-Weight Rooted Arborescence Problem................ 133468.3 DP-Heur: A Heuristic Approach to the MWRA Problem............... 133568.4 Ant Colony Optimization for the MWRA Problem...................... 133568.5 Experimental Evaluation................................................ 133768.6 Conclusions and Future Work .......................................... 1343References ....................................................................... 1343
69 An Intelligent Swarm of Markovian AgentsDario Bruneo, Marco Scarpa, Andrea Bobbio, Davide Cerotti,Marco Gribaudo ................................................................. 134569.1 Swarm Intelligence: A Modeling Perspective.......................... 134569.2 Markovian Agent Models................................................ 134669.3 A Consolidated Example: WSN Routing ................................ 1349
XLII Contents
69.4 Ant Colony Optimization ................................................ 135469.5 Conclusions .............................................................. 1358References ....................................................................... 1359
70 Honey Bee Social Foraging Algorithm for Resource AllocationJairo Alonso Giraldo, Nicanor Quijano, Kevin M. Passino .................... 136170.1 Honey Bee Foraging Algorithm ......................................... 136370.2 Application in a Multizone Temperature Control Grid ................ 136570.3 Results.................................................................... 137170.4 Discussion................................................................ 137370.5 Conclusions .............................................................. 1374References ....................................................................... 1374
71 Fundamental Collective Behaviors in Swarm RoboticsVito Trianni, Alexandre Campo ................................................. 137771.1 Designing Swarm Behaviours........................................... 137871.2 Getting Together: Aggregation ......................................... 137971.3 Acting Together: Synchronization ...................................... 138171.4 Staying Together: Coordinated Motion................................. 138371.5 Searching Together: Collective Exploration ............................ 138671.6 Deciding Together: Collective Decision Making ....................... 138871.7 Conclusions .............................................................. 1390References ....................................................................... 1391
72 Collective Manipulation and ConstructionLynne Parker ..................................................................... 139572.1 Object Transportation ................................................... 139572.2 Object Sorting and Clustering .......................................... 140172.3 Collective Construction and Wall Building............................. 140272.4 Conclusions .............................................................. 1404References ....................................................................... 1404
73 Reconfigurable RobotsKasper Støy....................................................................... 140773.1 Mechatronics System Integration ...................................... 140973.2 Connection Mechanisms ................................................ 141073.3 Energy .................................................................... 141173.4 Distributed Control ...................................................... 141273.5 Programmability and Debugging ...................................... 141773.6 Perspective............................................................... 141873.7 Further Reading ......................................................... 1419References ....................................................................... 1419
74 Probabilistic Modeling of Swarming SystemsNikolaus Correll, Heiko Hamann ............................................... 142374.1 From Bioligical to Artificial Swarms .................................... 142374.2 The Master Equation .................................................... 142474.3 Non-Spatial Probabilistic Models ...................................... 1424
Contents XLIII
74.4 Spatial Models: Collective Optimization ............................... 142874.5 Conclusion ............................................................... 1431References ....................................................................... 1431
Part G Hybrid Systems
75 A Robust Evolving Cloud-Based ControllerPlamen P. Angelov, Igor Škrjanc, Sašo Blažič ................................. 143575.1 Overview of Some Adaptive and Evolving Control Approaches ...... 143575.2 Structure of the Cloud-Based Controller............................... 143775.3 Evolving Methodology for RECCo ....................................... 143975.4 Simulation Study ........................................................ 144275.5 Conclusions .............................................................. 1447References ....................................................................... 1448
76 Evolving Embedded Fuzzy ControllersOscar H. Montiel Ross, Roberto Sepúlveda Cruz ............................... 145176.1 Overview ................................................................. 145276.2 Type-1 and Type-2 Fuzzy Controllers .................................. 145476.3 Host Technology ......................................................... 145776.4 Hardware Implementation Approaches ............................... 145876.5 Development of a Standalone IT2FC ................................... 146176.6 Developing of IT2FC Coprocessors ...................................... 146676.7 Implementing a GA in an FPGA ........................................ 146876.8 Evolving Fuzzy Controllers .............................................. 1470References ....................................................................... 1475
77 Multiobjective Genetic Fuzzy SystemsHisao Ishibuchi, Yusuke Nojima ................................................ 147977.1 Fuzzy System Design .................................................... 147977.2 Accuracy Maximization.................................................. 148277.3 Complexity Minimization ............................................... 148777.4 Single-Objective Approaches ........................................... 148977.5 Evolutionary Multiobjective Approaches .............................. 149177.6 Conclusion ............................................................... 1494References ....................................................................... 1494
78 Bio-Inspired Optimization of Type-2 Fuzzy ControllersOscar Castillo ..................................................................... 149978.1 Related Work in Type-2 Fuzzy Control ................................. 149978.2 Fuzzy Logic Systems ..................................................... 150078.3 Bio-Inspired Optimization Methods ................................... 150378.4 General Overview of the Area and Future Trends ..................... 150578.5 Conclusions .............................................................. 1506References ....................................................................... 1506
XLIV Contents
79 Pattern Recognition with Modular Neural Networksand Type-2 Fuzzy LogicPatricia Melin .................................................................... 150979.1 Related Work in the Area ............................................... 150979.2 Overview of Fuzzy Edge Detectors ...................................... 151079.3 Experimental Setup ..................................................... 151279.4 Experimental Results.................................................... 151379.5 Conclusions .............................................................. 1515References ....................................................................... 1515
80 Fuzzy Controllers for Autonomous Mobile RobotsPatricia Melin, Oscar Castillo ................................................... 151780.1 Fuzzy Control of Mobile Robots......................................... 151780.2 The Chemical Optimization Paradigm.................................. 151880.3 The Mobile Robot........................................................ 152180.4 Fuzzy Logic Controller ................................................... 152280.5 Experimental Results.................................................... 152380.6 Conclusions .............................................................. 1530References ....................................................................... 1530
81 Bio-Inspired Optimization MethodsFevrier Valdez .................................................................... 153381.1 Bio-Inspired Methods .................................................. 153381.2 Bio-Inspired Optimization Methods ................................... 153381.3 A Brief History of GPUs .................................................. 153581.4 Experimental Results.................................................... 153581.5 Conclusions .............................................................. 1538References ....................................................................... 1538
Acknowledgements .............................................................. 1539About the Authors ................................................................ 1543Detailed Contents................................................................. 1569Index ................................................................................ 1605
XLV
List of Abbreviations
Symbols
1-D one-dimensional2-D two-dimensional3-CNF-SAT three variables/clause-conjunctive normal
form-satisfiability3-D three-dimensional
A
A2A all-to-allAaaS analytics-as-a-serviceAANN auto-associative neural networkABC artificial bee colonyACC anterior cingulate cortexACO ant colony optimizationACP active categorical perceptionACS action-centered subsystemACS ant colony systemACT-R adaptive control of thought-rationalAD anomaly detectionADC analog digital converterADF additively decomposable functionADF automatically defined functionADGLIB adaptive genetic algorithm optimization
libraryAER address event representationAFPGA adaptive full POD genetic algorithmAFSA artificial fish swarm algorithmAI anomaly identificationAI artificial intelligenceAIC Akaike information criterionAICOMP comparable based AI modelAIGEN generative AI modelALCS anticipatory learning classifier systemALD approximate linear dependencyALM asset–liability managementALU arithmetic logic unitALU arithmetic unitAM amplitude modulationamBOA adaptive variant of mBOAAMPGA adaptive mixed-flow POD genetic
algorithmAMS anticipated mean shiftAMT active media technologyANN artificial neural network
ANOVA analysis of varianceANYA Angelov–YagerAP alternating-position crossoverAP automatic programmingAPA affine projection algorithmAPI application programming interfaceAPS aggregation pheromone systemAPSD auto power spectral densityAR approximate reasoningAR average rankingARD automatic relevance determinationARGOT adaptive representation genetic
optimization techniqueARMOGA adaptive range MOGAASIC application-specific integrated circuitASP answer-set programmingATP adenosine triphosphateAUC area under curveAUC area under ROC curveAVITEWRITE adaptive vector integration to endpoint
handwriting
B
BBB blood brain barrierBCI brain–computer interfaceBCO bee colony optimizationBDAS Berkeley data analytics stackBER bit error rateBeRoSH behavior-based multiple robot system
with host for object manipulationBFA basic fuzzy algebraBG basal gangliaBIC Bayesian information criterionBINCSP binary constraint satisfaction problemBioHEL bioinformatics-oriented hierarchical
evolutionary learningBKS Bandler–Kohout subproductBLB bag of little bootstrapBMA Bayes model averagingBMDA bivariate marginal distribution algorithmBMF binary matrix factorizationBMI brain–machine interfaceBnB branch and boundBOA Bayesian optimization algorithmBP bereitschafts potential
XLVI List of Abbreviations
BP back-propagationBPTT back-propagation through timeBSB base system builderBSD bipolar satisfaction degreeBSS blind source separationBYY Bayesian Yin-Yang
C
c-granule complex granuleCA cellular automataCA classification accuracyCA complete F-transform-based fusion
algorithmCAD computer-aided designCAE contrastive auto-encoderCAM computer-assisted manufacturingCART classification analysis and regression treeCBLS constraint-based local searchCBR case-based reasonerCBR case-based reasoningCC coherence criterionCCF cross correlation functionCCG controlling crossed genesCD contrastive divergenceCEA cellular evolutionary algorithmCEBOT cellular robotCF collaborative filteringCF compact flashcf convergence factorCFD computational fluid dynamicsCFG context-free grammarCFS correlation feature selectionCG center of gravityCG Cohen–GrossbergcGA compact genetic algorithmCGP Cartesian GPCI computational intelligenceCIP cross information potentialCIS Computational Intelligence SocietyCLB configurable logic blockclk clockCLM component level modelCMA cingulate motor areaCMA covariance matrix adaptationCML coupled map latticecMOEA cellular MOEACMOS complementary
metal-oxide-semiconductorCNF conjunctive normal form
CNGM computational neuro-genetic modelingCNN cellular neural networkCNS central nervous systemCOA center of areaCOG center of gravityCOGIN coverage-based genetic inductionCOP cluster of processorsCOP constrained optimization problemCORE Computing Research and Educationcos center of setCOW cluster of workstationsCP constraint programmingCP contrapositive symmetryCP net conditional preference networkCPF centralized Pareto frontCPG central pattern generatorCPSD cross power spectral densityCPT cummulative prospect theoryCPU central processing unitCR commonsense reasoningCR control registerCRA chemical reaction algorithmCRI compositional rule of inferenceCS cell savingCS1 cognitive systemCSA contractual service agreementCSA cumulative step-size adaptationCSM covariate shift minimizationCSP common spatial patternCSP constraint satisfaction problemCST class-shape transformationCST corticospinal tractCTMC continuous-time finite Markov chainCUDA compute unified device architectureCW computing with wordsCW control wordCWW computing with wordsCX cycle crossover
D
DA dopamineDACE design and analysis of computer
experimentsDAE denoising auto-encoderDAG directed acyclic graphDAL logic for data analysisDB databaseDBN deep belief networkdBOA decision-graph BOA
List of Abbreviations XLVII
DC direct currentDC/AD change/activate-deactivateDCA de-correlated component analysisDE differential evolutiondEA distributed evolutionary algorithmDENFIS dynamic neuro-fuzzy inference systemdeSNN dynamic eSNNDEUM density estimation using Markov random
fields algorithmDEUM distribution estimation using Markov
random fieldsDGA direct genetic algorithmDIC deviance information criterionDL deep learningDLPFC dorsolateral prefrontal cortexDLR German Aerospace CenterDLS dynamic local searchDM displacement mutation operatorDM decision makerDMA direct memory accessdMOEA distributed MOEADNA deoxyribonucleic acidDNF disjunctive normal formDNN deep neural networkDOE design of experimentDOF degree of freedomDP dynamic programmingDPF distributed Pareto frontDPLL Davis–Putnam–Logemann–LovelandDPR dynamic partial reconfigurationDRC domain relational calculusDREAM distributed resource evolutionary
algorithm machineDRRS dynamically reconfigurable robotic
systemDRS dominance resistant solutionDRSA dominance-based rough set approachDSA data space adaptationDSMGA dependency-structure matrix genetic
algorithmDSP digital signal processingDSP digital signal processorDSS decision support systemdtEDA dependency-tree EDADTI diffusion tensor imagingDTLZ Deb–Thiele–Laumanns–ZitzlerDTRS decision-theoretic rough setDW data word
E
EA evolutionary algorithmEAPR early access partial reconfigurationEBNA estimation of Bayesian network
algorithmEC embodied cognitionEC evolutionary computationEC evolutionary computingECGA extended compact genetic algorithmECGP extended compact genetic programmingECJ Java evolutionary computationECoG electrocorticographyECOS evolving connectionist systemEDA estimation of distribution algorithmEDP estimation of distribution programmingEEG electroencephalogramEEG electroencephalographyEFRBS evolutionary FRBSEFuNN evolving fuzzy neural networkEGA equilibrium genetic algorithmEGNA estimation of Gaussian networks
algorithmEGO efficient global optimizationEHBSA edge histogram based sampling algorithmEHM edge histogram matrixEI expected improvementEKM enhanced KMEKMANI enhanced Karnik–Mendel algorithm with
new initializationELSA evolutionary local selection algorithmEM exchange mutation operatorEM expectation maximizationEMG electromyographyEMNA estimation of multivariate normal
algorithmEMO evolutionary multiobjective optimizationEMOA evolutionary multiobjective algorithmEMSE excess mean square errorEODS enhanced opposite directions searchingEP evolutionary programmingEP exchange propertyEPTV extended possibilistic truth valueER edge recombinationERA epigenetic robotics architectureERA Excellence in Research for AustraliaERD event-related desynchronizationERM empirical risk minimizationERS event-related synchronizationES embedding systemES evolution strategy
XLVIII List of Abbreviations
ESA enhanced simple algorithmESN echo state networkeSNN evolving spiking neural networkESOM evolving self-organized mapETS evolving Takagi–Sugeno systemEV extreme valueEvoStar Main European Events on Evolutionary
ComputationEvoWorkshops European Workshops on Applications of
Evolutionary ComputationEW-KRLS exponentially weighted KRLSEX-KRLS extended kernel recursive least square
F
FA factor analysisFA firefly algorithmFA fractional anisotropyFA-DP fitness assignment and diversity
preservationFATI first aggregation then inferenceFB-KRLS fixed-budget KRLSFCA formal concept analysisFCM fuzzy c-means algorithmFDA factorized distribution algorithmFDRC fuzzy domain relational calculusFDT fuzzy decision treeFEMO fair evolutionary multi-objective
optimizerFGA fuzzy generic algorithmFIM fuzzy inference mechanismFIM fuzzy instance based modelFIR finite impulse responseFIS fuzzy inference systemFIS1 type-1 fuzzy inference systemFIS2 type-2 fuzzy inference systemFITA first inference then aggregationFL fuzzy logicFLC fuzzy logic controllerFlexCo flexible coprocessorFLP fuzzy linear programmingFLS fuzzy logic systemFM fuzzy modelingFMG full multi-gridFMM finite mixture modelFMM fuzzy mathematical morphologyfMRI functional magneto-resonance imagingFNN fuzzy neural networkFNN fuzzy nearest neighborFOM full-order model
FOU footprint of uncertaintyFPGA field programmable gate arrayFPGA full POD genetic algorithmFPID fuzzy PIDFPM fractal prediction machineFPSO fuzzy particle swarm optimizationFPU floating point unitFQL fuzzy query languageFRB fuzzy rule-basedFRBCS fuzzy rule-based classification systemsFRBS fuzzy rule-based systemFRC fuzzy-rule based classifierFRI fuzzy relational inferenceFS fuzzy setFS fuzzy systemFSVM fuzzy support vector machineFTR F-transform image compressionFURIA unordered fuzzy rule induction algorithmFX foreign exchange
G
GA general achievementGA genetic algorithmGABA gamma-aminobutyric acidGACV generalized approximate cross-validationGAGRAD genetic algorithm gradientGAOT genetic algorithm optimization toolboxGC granular computingGCP graph coloring problemGE grammatical evolutionGECCO Genetic and Evolutionary Computation
ConferenceGEFRED generalized fuzzy relational databaseGFP green fluorescent proteinGFS genetic fuzzy systemGGA grouping genetic algorithmGGP grammar-based genetic programmingGM gray matterGMP generalized modus ponensGMPE grammar model-based program evolutionGP genetic algorithmGP genetic programmingGP Gaussian processGPCR g-protein coupled receptorGPGPU general-purpose GPUGPi globus pallidusGPIO general-purpose input/output interfaceGPS genetic pattern searchGPU graphics processing unit
List of Abbreviations XLIX
GPX greedy partition crossoverGRASP greedy randomized adaptive search
procedureGRBM Gaussian RBMGRN gene/protein regulatory networkGRN gene regulatory networkGT2 generalized T2FSGWAS genome-wide association studiesGWT global workspace theory
H
H-PIPE hierarchical probabilistic incrementalprogram evolution
hBOA hierarchical BOAHCF hyper-cube frameworkHCwL hill climbing with learningHDL hardware description languageHFB higher frequency bandHH Hodgkin–HuxleyHM health managementHMM hidden Markov modelHPC high-performance computingHS Hilbert spaceHSS heuristic space searchHT Hough transformHW hardwareHWICAP hardware internal configuration access
point
I
i.i.d. independent, identically distributedIASC iterative algorithm with stop conditionIB indicator basedIBEA indicator-based evolutionary algorithmiBOA incremental Bayesian optimization
algorithmIC intelligent controllerIC interrupt controllerICA independent component analysisICAP internal configuration access pointICE induced chromosome element exchangerICML International Conference on Machine
LearningIDA intelligent distribution agentIDEA iterated density estimation evolutionary
algorithmIE inference engine
IEEE Institute of Electrical and ElectronicsEngineers
IF intuitionistic fuzzyIFA independent factor analysisIFVS interval valued fuzzy setIG iterated greedyIGR interactive granular computingIHA iterative heuristic algorithmILS iterated local searchIN interneuronINCF International Neuroinformatics
Coordinating FacilityIO-HMM input/output hidden Markov modelIOB input/output blockIoT internet of thingsIP identity principleIP inductive programmingIP intellectual propertyIPIF intellectual property interfaceIPL inferior parietal lobeIPOP increasing population sizeIRGC interactive rough granular computingIRLS iteratively re-weighted least squaresIRSA indiscernibility-based rough set approachISE Integrated Synthesis EnvironmentISI inter-spike intervalISM insertion mutation operatorIT2 interval type-2IT2FC interval T2FCIT2FS interval T2FSITAE integral time absolute errorITL information theoretic learningIUMDA incremental univariate marginal
distribution algorithmIVM inversion mutation operator
J
JDEAL Java distributed evolutionary algorithmslibrary
JEGA John Eddy genetic algorithmJMAF java multi-criteria and multi-attribute
analysis framework
K
KAF kernel adaptive filterKAPA kernel affine projection algorithmKB knowledge baseKDD knowledge discovery and data mining
L List of Abbreviations
KGA Kriging-driven genetic algorithmKKT Karush–Kuhn–TuckerKL Kullback–LeiblerKLMS kernel least mean squareKM Karnik–MendelKMC kernel Maximum CorrentropyKNN k nearest neighborKPCA kernel principal component analysisKRLS kernel recursive least squareKUR Kurswae
L
LAN local networkLASSO least absolute shrinkage and selection
operatorLB logic blockLCS learning classifier systemLDA latent Dirichlet allocationLDA linear discriminant analysisLDS limited discrepancy searchLED light emitting diodeLEM learning from examples moduleLERS learning from examples using rough setsLFA local factor analysisLFB lower frequency bandLFDA learning FDALFM linguistic fuzzy modelingLFP local field potentialLGP linear GPLHS latin hypercube samplingLI law of importationLIFM leaky integrate-and-fireLLE liquid–liquid equilibriumLMI linear matrix inequalitiesLMS least mean squareLNS large neighborhood searchLO leading oneLOCVAL locational valueLOO leave-one-outLOOCV leave-one-out cross-validationLOTZ leading ones trailing zeroesLP logic programmingLQR linear-quadratic regulatorLR logistic regressionLRP lateralized readiness potentialLS least squareLS local searchLSM liquid state machineLSTM long short term memory
LT linguistic termLUT look-up tableLV linguistic variableLVT linguistic-variable-termLWPR locally-weighted projection regression
algorithmLZ leading zero
M
M1 motor cortexM2M machine-to-machineMA Markovian agentMA memetic algorithmMAE mean of the absolute errorMAFRA Java mimetic algorithms frameworkMAM Markovian agent modelMAMP multiple algorithms, multiple problemsMAMS multiple algorithms and multiple
problem instancesMAP maximum a posterioriMARS multivariate adaptive regression splinesMASP multiple algorithms and one single
problemmBOA mixed Bayesian optimization algorithmMCA minor component analysisMCDA multi-criteria decision analysisMCDA multiple criteria decision aidingMCDM multiple criteria decision-makingMCS maximum cardinality searchMCS meta-cognitive subsystemMDL minimum description lengthMDP Markov decision processMDS multidimensional scalingMEG magnetoencephalogramMEG magnetoencephalographyMEL minimal epistemic logicMF membership functionMG Mackey–GlassMG morphological gradientMH metaheuristicMIL multi-instance learningMIMIC mutual information maximizing input
clusteringMIML multi-instance, multi-label learningMISO multiple inputs-single outputMKL multiple kernel learningML machine learningML maximum likelihoodMLEM2 modified LEM2 algorithm
List of Abbreviations LI
MLP multilayer perceptronMLR multiple linear regressionMLR multi-response linear regressionMM mathematical morphologyMMA multimemetic algorithmMMAS MAX-MIN ant systemMMEA model-based multiobjective evolutionary
algorithmMMLD man–machine learning dilemmaMN-EDA Markov network EDAMNN memristor-based neural networkMNN modular neural networkMOAMO multiobjectivization-assisted multimodal
optimizationMOE multiobjective evolutionaryMoE mixture of expertsMOEA multiobjective evolutionary algorithmMOEA/D multiobjective evolutionary algorithm
based on decompositionMOGA multiobjective genetic algorithmMoGFS multiobjective genetic fuzzy systemMOM mean of maximaMOMGA multi-objective messy GAMOO multi-objective optimizationMOP many-objective optimization problemMOP multiobjective problemMOP multiobjective optimization problemMOPSO multiobjective particle swarm
optimizationMOSAIC modular selection and identification for
controlMOSES meta-optimizing semantic evolutionary
searchMOT movement timeMPE mean percentage errorMPE most probable explanationMPGA mixed-flow POD genetic algorithmMPI message passing interfaceMPM marginal product modelMPP massively parallel machineMPS multiprocessor systemMR maximum rankingMRCP movement-related cortical potentialsMRI magnetic resonance imagingmRMR minimal-redundancy-maximal-relevancemRNA messenger RNAMRNN memristor-based recurrent neural
networkMS master/slaveMS motivational subsystemMSA minor subspace analysis
MSE mean square errorMSG max-set of Gaussian landscape generatormsMOEA master–slave MOEAMT medial temporalMTFL multi-task feature learningMTL multi-task learningMV maximum valueMWRA minimum-weight rooted arborescence
N
NACS non-action-centered subsystemNASA National Aeronautics and Space
AdministrationNC neural computationNC novelty criterionNC numerical controlNCL negative correlation learningNDS nonlinear dynamical systemsNEAT neuro-evolution of augmenting
topologiesNES natural evolution strategyNeuN neuronal nuclei antibodyNFA non-Gaussian factor analysisNFI neuro-fuzzy inference systemNFL no free lunchNHBSA node histogram based sampling
algorithmNIL nondeterministic information logicNIPS neural information processing systemNLMS normalized LMSNLPCA nonlinear principal componentsNMF negative matrix and tensor factorizationNMF nonnegative matrix factorizationNN neural networkNOW networks of workstationNP neutrality principleNP nondeterministic polynomial-timeNPV net present valueNR noise reductionNS negative slopeNSGA nondominated sorting genetic algorithmNSPSO nondominated sorting particle swarm
optimizationNURBS nonuniform rational B-spline
O
ODE ordinary differential equationOEM original equipment manufacturer
LII List of Abbreviations
OKL online kernel learningOLAP online analytical processingOM operational momentumOMA ordered modular averageOP ordering propertyOPB on-chip peripheral busOPL open programming languageOR operations researchOR operational researchOS overshootOWA ordered weighted averageOWMax ordered weighted maximumOX1 order crossoverOX2 order-based crossover
P
PAC probably approximately correctPAES Pareto-archived evolution strategyPAR place and routePBC perception-based computingPBIL population-based incremental learningPbO programming by optimizationPC probabilistic computingPC-SAFT perturbed chain statistical associating
fluid theoryPCA principal component analysisPCVM probabilistic classifier vector machinePD Parkinson diseasePD proportional-differentialPDDL planning domain definition languagePDE partial differential equationpdf probability density functionPDGP parallel and distributed GPPEEL program evolution with explicit
learningPERT program evaluation and review
techniquePESA Pareto-envelope based selection
algorithmPET positron emission tomographyPFC Pareto front computationPFC prefrontal cortexPFM precise fuzzy modelingPHM prognostics and health managementPIC peripheral interface controllerPID proportional-integral-derivativePII probabilistic iterative improvementPIPE probabilistic incremental program
evolution
PLA programmable logic arrayPLB processor local busPLS partial least squarepLSA probabilistic latent semantic analysisPLV phase lock valuePM parallel modelPMBGA probabilistic model-building genetic
algorithmPMC premotor cortexPMI partial mutual informationPMX partially-mapped crossoverPN pyramidal neuronPNS peripheral nervous systemPOD proper orthogonal decompositionPoE product of expertsPOR preference order relationPOS position-based crossoverPP parallel platformPPSN parallel problem solving in naturePR partial reconfigurationPRAS polynomial-time randomized
approximation schemePRM partially reconfigurable modulePRODIGY program distribution estimation with
grammar modelPRR partially reconfigurable regionPS pattern searchPSA principal subspace analysisPSCM problem-space computational modelPSD power spectral densityPSD predictive sparse decompositionPSEA Pareto sorting evolutionary algorithmPSNR peak signal-to-noise ratioPSO particle swarm optimizationPSS problem space searchPSTH peri-stimulus-time histogramPTT pursuit-tracking taskPV principal valuePVS persistent vegetative statePWM pulse width modulation
Q
Q–Q quantile–quantileQAP quadratic assignment problemQeSNN quantum-inspired eSNNQIP quadratic information potentialQKLMS quantized KLMSQP quadratic programming
List of Abbreviations LIII
R
r.k. reproducing kernelRAF representable aggregation functionRAM random access memoryRANS Reynolds-averaged Navier–StokesRB rule baseRBF radial basis functionRBM Boltzmann machinerBOA real-coded BOARECCo robust evolving cloud-based controllerRecNN recursive neural networkREGAL relational genetic algorithm learnerREML restricted maximum likelihood
estimatorRET relevancy transformationRFID radio frequency identificationRFP red fluorescent proteinRGB red-green-blueRGN random generation numberRHT randomized Hough transformRII randomized iterative improvementRISC reduced intstruction set computerRKHS reproducing kernel Hilbert spaceRL reinforcement learningRLP randomized linear programmingRLS randomized local searchRLS recursive least squareRM-MEDA regularity model based multiobjective
EDARMI remote method invocationRMSE root-mean-square errorRMSEP root-mean-square error of predictionRMTL regularized multi-task learningRN regularization networkRNA ribonucleic acidRNN recurrent neural networkROC receiver operating characteristicROI region of interestROM read only memoryROM reduced-order modelROS robot operating systemRP readiness potentialRPC remote procedure callRPCL rival penalized competitive learningRS rough setrst resetRT reaction timeRT real-timeRTL register transfer logicRTRL real-time recurrent learning
RUL remaining useful lifeRWS roulette wheel selection
S
S-ACO simple ant colony optimizationS-bit section-bitS3VM semi-supervised support vector
machineSA simple F-transform-based fusion
algorithmSA simulated annealingSAE sparse auto-encoderSAMP one single algorithm and multiple
problemsSARSA state-action-reward-state-actionSASP one single algorithm and one single
problemSAT satisfiabilitySBF subspace-based functionSBO surrogate-based optimizationSBR similarity based reasoningSBS sequential backward selectionSBSO surrogate based shape optimizationSBX simulated binary crossoverSC soft computingSC surprise criterionSCH schoolSCNG sparse coding neural gasSD structured dataSDE stochastic differential equationSDPE standard deviation percentage errorSEAL simulated evolution and learningSEMO simple evolutionary multi-objective
optimizerSF scaling factorSFS sequential forward selectionSG-GP stochastic grammar-based genetic
programmingSHCLVND stochastic hill climbing with learning by
vectors of normal distributionSI swarm intelligenceSIM simple-inversion mutation operatorSISO single input single outputSLAM simultaneous localization and mappingSLF superior longitudinal fasciculusSLS stochastic local searchSM scramble mutation operatorSM surrogate modelSMA supplementary motor area
LIV List of Abbreviations
SMO sequential minimum optimizationSMP symmetric multiprocessorSMR sensorimotor rhythmsMRI structural magnetic resonance imagingSMS-EMOA S-metric selection evolutionary
multiobjective algorithmSNARC spatial–numerical association of response
codeSNE stochastic neighborhood embeddingSNP single nucleotide polymorphismSNR signal-noise-ratioSOC self-organized criticalitySOFM self-organized feature mapsSOFNN self-organizing fuzzy neural networkSOGA single-objective genetic algorithmSOM self-organizing mapSPAM set preference algorithm for
multiobjective optimizationSPAN spike pattern association neuronSPD strictly positive definiteSPEA strength Pareto evolutionary algorithmSPOT sequential parameter optimization
toolboxSPR static partial reconfigurationSQL structured query languageSR stochastic resonanceSR symbolic regressionSRD standard reference datasetSRF strength raw fitnessSRM spike response modelSRM structural risk minimizationSRN simple recurrent networkSRT serial reaction timeSSM state–space modelSSOCF subset size-oriented common featuresSSSP single-source shortest path problemStdGP standard GPSTDP spike-timing dependent plasticitySTDP spike-timing dependent learningSTGP strongly typed GPSU single unitSURE-REACH sensorimotor, unsupervised,
redundancy-resolving controlarchitecture
SUS stochastic universal samplingSVaR simplified value at riskSVC support vector classificationSVD singular value decompositionSVM support vector machineSW softwareSW-KRLS sliding window KRLS
T
T1 type-1T1FC type-1 fuzzy controllerT1FS type-1 fuzzy setT2 type-2T2FC type-2 fuzzy controllerT2FS type-2 fuzzy setT2IC type-2 intelligent controllerT2MF type-2 membership functionTAG3P tree adjoining grammar-guided genetic
programmingTD temporal differenceTDNN time delay neural networkTET total experiment timeTFA temporal factor analysisTGBF truncated generalized Bell functionTN thalamusTOGA target objective genetic algorithmTR type reducerTRC tuple relational calculusTS tabu searchTS time savingTSK Takagi–Sugeno–KangTSP traveling salesman problemTTGA trainable threshold gate arrayTWNFI transductive weighted neuro-fuzzy
inference system
U
UART universal asynchronousreceiver/transmitter
UAV unmanned aerial vehicleUCF user constraint fileUCS supervised classifier systemUCX uniform cycle crossoverUMDA univariate marginal distribution algorithmUML universal modeling languageUPMOPSO user-preference multiobjective PSOUS EPA United States Environmental Protection
AgencyUW underwriter
V
VB variational BayesVC Vapnik–ChervonenkisVC variable consistencyVCR variance ratio criterionVEGA vector-evaluated GA
List of Abbreviations LV
VHDL VHSIC hardware description languageVHS virtual heading systemVHSIC very high speed integrated circuitVLNS very large neighborhood searchVLPFC ventrolateral prefrontal cortexVLSI very large scale integrationVND variable neighborhood descentVNS variable neighborhood searchVPRSM variable precision rough set modelVQ vector quantizationVQRS vaguely quantified rough set
W
W2T wisdom web of thingsWAN wide area networkWC Wilson–CowanWEP weight error powerWFG walking fish groupWisTech Wisdom TechnologyWM white matter
WM working memoryWSN wireless sensor networkWT Wu–TanWTA winner-take-allWWKNN weighted-weighted nearest neighbor
X
XACS x-anticipatory classifier systemXB Xie-Beni cluster validity indexXCS X classifier systemXCSF XCS for function approximationxNES exponential natural evolution strategyXPS Xilinx platform studioXSG Xilinx system generator
Z
ZCS zeroth level classifier systemZDT Zitzler–Deb–ThieleZEN Zonal Euler–Navier–Stokes