introduction to evolutionary computing

8/12/2019 Introduction to Evolutionary Computing

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C H A P T E R 1

INTRODUCTION


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LECTURER

Dr Zeratul Izzah Mohd Yusoh

Industrial Computing

Leel !" #I $ing

%& ''1 &(%%


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Positioning o) EC and the *asi+ EC metaphor

Histori+al perspe+tie

,iologi+al inspiration-

Dar$inian eolution theor. /simpli)ied0

2eneti+s /simpli)ied0

Motiation )or EC

3hat +an EC do- e4amples o) appli+ation areas

CONTENTS


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Borg Vogons

Biotop

Art

Life Sciences Social Sciences

Mathematics Physics

Software Engineering

Neural Nets Evolutionary Computing Fuy Systems

Computational !ntelligence etc

Computer Science etc

E"act Sciences etc

Science Politics Sports etc

Society Stones # Seas etc

Earth etc

$niverse

%ou are here

POSITIONING OF EC


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POSITIONING OF EC

EC is part o) +omputer s+ien+e

EC is not part o) li)e s+ien+es5*iolog.

,iolog. deliered inspiration and terminolog.

EC +an *e applied in *iologi+al resear+h


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POSITIONING OF EA


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SUBCATEGORIES OF EA


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THE MAIN EVOLUTIONARY COMPUTINGMETAPHOR

EVOLUTION

Enironment

Indiidual

6itness

PROBLEM SOLVING

Pro*lem

Candidate 7olution

8ualit.

Quality

chance for seeding new solutions

Fitness chances for survival and reproduction


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BRIEF HISTORY 1: THE ANCESTORS

9 1:;


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BRIEF HISTORY 2: THE RISE OF EC

1985: first international conference (I!"#

199$: first international conference in %urope (&&'#

199): first scientific % *ournal (+I, &ress#

199-: launch of %uropean % .esearch etwor/ %voet


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DARWINIAN EVOLUTION 1:SURVIVAL OF THE FITTEST

All enironments hae )inite resour+es/i@e@" +an onl. support a limited num*er o) indiiduals

Li)e)orms hae *asi+ instin+t5 li)e+.+les geared to$ards

reprodu+tion There)ore some Bind o) sele+tion is ineita*le

Those indiiduals that +ompete )or the resour+es most

e))e+tiel. hae in+reased +han+e o) reprodu+tion ote- )itness in natural eolution is a deried" se+ondar.

measure" i@e@" $e /humans assign a high )itness to

indiiduals $ith man. o))spring


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DARWINIAN EVOLUTION:SUMMARY

Population +onsists o) dierse set o) indiiduals

Com*inations o) traits that are *etter adapted tendto in+rease representation in population

Indiiduals are =units o) sele+tion>

ariations o++ur through random +hanges .ielding+onstant sour+e o) diersit." +oupled $ith sele+tion

means that-

Population is the =unit o) eolution> ote the a*sen+e o) =guiding )or+e>


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ADAPTIVE LANDSCAPE METAPHOR(WRIGHT, 1932)

9 Can enisage population $ith n traits as e4isting in

a n+1dimensional spa+e /lands+ape $ith height+orresponding to )itness

9 Ea+h di))erent indiidual /phenot.pe represents asingle point on the lands+ape

9 Population is there)ore a =+loud> o) points" moingon the lands+ape oer time as it eoles adaptation


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EXAMPLE WITH TWO TRAITS


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NATURAL GENETICS

The in)ormation reFuired to *uild a liing organism is

+oded in the DA o) that organism 2enot.pe /DA inside determines phenot.pe

2enes phenot.pi+ traits is a +omple4 mapping

ne gene ma. a))e+t man. traits /pleiotrop. Man. genes ma. a))e+t one trait /pol.gen.

7mall +hanges in the genot.pe lead to small+hanges in the organism /e@g@" height" hair +olour


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GENES AND THE GENOME

2enes are en+oded in strands o) DA +alled

+hromosomes

In most +ells" there are t$o +opies o) ea+h

+hromosome /diploid.

The +omplete geneti+ material in an indiidualGsgenot.pe is +alled the 2enome

3ithin a spe+ies" most o) the geneti+ material is the

same


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EXAMPLE: HOMO SAPIENS

Human DA is organised into +hromosomes

Human *od. +ells +ontains !' pairs o)+hromosomes $hi+h together de)ine the ph.si+alattri*utes o) the indiidual-


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REPRODUCTIVE CELLS

2ametes /sperm and egg +ells +ontain !'

indiidual +hromosomes rather than !' pairs Cells $ith onl. one +op. o) ea+h +hromosome are

+alled Haploid

2ametes are )ormed *. a spe+ial )orm o) +ellsplitting +alled meiosis

During meiosis the pairs o) +hromosome undergo anoperation +alled crossing-over


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CROSSINGOVER DURING MEIOSIS

Chromosome pairs align and dupli+ate Inner pairs linB at a centromere and s$ap partso) themseles

0utcoe is one copy of aternal2paternalchroosoe plus two entirely new co3inations

"fter crossing4over one of each pair goes into each

gaete


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FERTILISATION

'per cell fro Father %gg cell fro +other

ew person cell (ygote#


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AFTER FERTILISATION

e$ z.gote rapidl. diides et+ +reating man. +ells

all $ith the same geneti+ +ontents Although all +ells +ontain the same genes"

depending on" )or e4ample $here the. are in the

organism" the. $ill *ehae di))erentl. This pro+ess o) di))erential *ehaiour during

deelopment is +alled ontogenesis

All o) this uses" and is +ontrolled *." the same

me+hanism )or de+oding the genes in DA


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MUTATION

++asionall. some o) the geneti+ material +hanges

er. slightl. during this pro+ess /repli+ation error This means that the +hild might hae geneti+

material in)ormation not inherited )rom either parent

This +an *e +atastrophi+- o))spring in not ia*le /most liBel.

neutral- ne$ )eature not in)luen+es )itness

adantageous- strong ne$ )eature o++urs

Redundan+. in the geneti+ +ode )orms a good$a. o) error +he+Bing


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MOTIVATIONS FOR EC: 1

ature has al$a.s sered as a sour+e o)

inspiration )or engineers and s+ientists

The *est pro*lem soler Bno$n in nature is-

the /human *rain that +reated =the $heel" e$ YorB"

$ars and so on> /a)ter Douglas AdamsG Hit+hHiBers2uide

the eolution me+hanism that +reated the human *rain/a)ter Dar$inGs rigin o) 7pe+ies

Ans$er 1 neuro+omputing Ans$er ! eolutionar. +omputing


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PROBLEM TYPE 1 : OPTIMISATION

3e hae a model o) our s.stem and seeB inputsthat gie us a spe+i)ied goal

e6g6

7 tie ta3les for university call center or hospital

7 design specifications etc etc


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0ptiisation eaple 1: niversity tieta3ling

%norously 3ig search space

,ieta3les ust 3e good

;!ood< is defined 3y a nu3er

of copeting criteria

,ieta3les ust 3e feasi3le

=ast a*ority of search space

is infeasi3le


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PROBLEM TYPES 2: MODELLING

3e hae +orresponding sets o) inputs outputsand seeB model that deliers +orre+t output )oreer. Bno$n input

%volutionary achine learning


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+odelling eaple: loan applicant crediti3ility

>ritish 3an/ evolvedcredita3ility odel to predict

loan paying 3ehavior of new

applicants

%volving: prediction odels

Fitness: odel accuracy onhistorical data


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PROBLEM TYPE 3: SIMULATION

3e hae a gien model and $ish to Bno$ theoutputs that arise under di))erent input +onditions

0ften used to answer ;what4if< ?uestions in evolving

dynaic environents

e6g6 %volutionary econoics "rtificial @ife


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SIMULATION EXAMPLE:EVOLVING ARTIFICIAL SOCIETIES

7imulating trade" e+onomi++ompetition" et+@ to +ali*ratemodels

se models to optimisestrategies and poli+ies

Eolutionar. e+onom.

7urial o) the )ittest isuniersal /*ig5small )ish

introduction to evolutionary computing

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