2012 - h larget - genetic algorithms used in timetable management

Genetic Algorithms used in Timetable Management

Henri LARGET

April 2012

The work contained within this document has been submitted by the student in partial fulfilment of the requirement of their course and award

By

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Abstract

This paper is about the prototype of a computer application that uses Genetic Algorithms (GAs) in

timetable management problems. It is an approach about GAs, more generally the field of Artificial

intelligence that explores the field of Automated scheduling. The project is for a bachelor’s degree.

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Acknowledgments

I would like to acknowledge the contribution of Dr Faiyaz Doctor, from Coventry University, who

suggested this topic and supervised me during its realisation.

I would also like to acknowledge the Informatics department of Université de Savoie at the IUT

Annecy in France and especially Ms Christine RIEU permitting me to complete my bachelor’s degree

in a foreign country.

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Abstract(.............................................................................................................................................................................(1!

Acknowledgments(..........................................................................................................................................................(2!

1.! Introduction(.............................................................................................................................................................(5!

2.! Timetable(management(and(genetic(Algorithms(........................................................................................(6!

2.1.! Timetable(management(................................................................................................................................................(6!

2.1.1.! What!is!a!timetable?!.....................................................................................................................................................................!6!

2.1.2.! What!is!a!“good”!timetable?!......................................................................................................................................................!6!

2.1.3.! What!is!the!actual!timetable!management!problem?!....................................................................................................!8!

2.2.! Genetic(Algorithms(.........................................................................................................................................................(8!

2.2.1.! History!................................................................................................................................................................................................!8!

2.2.2.! Definitions!........................................................................................................................................................................................!8!

2.2.3.! Data!encoding!and!decoding!...................................................................................................................................................!10!

2.2.4.! Initial!Population!.........................................................................................................................................................................!11!

2.2.5.! Evaluation!of!the!population!–!Fitness!function!............................................................................................................!11!

2.2.6.! Crossover!evolution!....................................................................................................................................................................!12!

2.2.7.! Mutation!..........................................................................................................................................................................................!14!

2.2.8.! New!population!and!loop!.........................................................................................................................................................!14!

3.! Prototyping(............................................................................................................................................................(17!

3.1.! Data(stored(and(encoding(.........................................................................................................................................(17!

3.2.! Fitness(function(and(loop(ending(...........................................................................................................................(19!

3.2.1.! Constraints!.....................................................................................................................................................................................!19!

3.2.2.! Evaluation!of!fitness!...................................................................................................................................................................!20!

3.2.3.! End!of!loop!......................................................................................................................................................................................!20!

3.3.! Conception(.........................................................................................................................................................(20!

4.! Project(Management(..........................................................................................................................................(21!

4.1.! The(project(.....................................................................................................................................................................(21!

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4.1.1.! Scheduling!the!project!...............................................................................................................................................................!21!

4.1.2.! Technology!choice!.......................................................................................................................................................................!21!

5.! Conclusions(...........................................................................................................................................................(22!

5.1.! The(prototype(................................................................................................................................................................(22!

5.2.! Genetic(Algorithms(......................................................................................................................................................(22!

5.2.1.! Genetic!algorithms!opportunities!.........................................................................................................................................!22!

5.2.2.! Limits!of!Genetic!Algorithms!..................................................................................................................................................!22!

5.3.! Personal(Conclusion(....................................................................................................................................................(23!

6.! Literature(review(and(bibliography(.............................................................................................................(24!

6.1.! Literature(review(.........................................................................................................................................................(24!

6.2.! Bibliography(..................................................................................................................................................................(24!

7.! Appendices(............................................................................................................................................................(25!

Appendix(1a(P(project(proposal(...........................................................................................................................................(25!

Appendix(1b(P(project(proposal(marking(.........................................................................................................................(26!

Appendix(2(P(PowerPoint(presentation(............................................................................................................................(27!

Appendix(3(P(class(diagram(...................................................................................................................................................(28!

Appendix(4(P(Prototype(results(...........................................................................................................................................(29!

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1. Introduction

This paper is about Genetic Algorithms used in timetable management at university. The objectives

of this project are, first, to introduce Genetic Algorithm and, secondly, to use it to solve a timetable

management problem.

The objectives are to define what are Genetic Algorithms and how it works, to define what is

timetable management and its actual problems and to make a prototype of an automated timetable

management system without using the classical approach but the Genetic Algorithm.

The current timetable system in Coventry University is estimated adequate. This thesis does not

propose an alternative.

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2. Timetable management and genetic Algorithms

2.1. Timetable management

2.1.1. What is a timetable?

A timetable is a system used to schedule time.

A School timetable is a table setup to coordinate students, teachers and rooms for a given period.

The act of planning tasks or activities to improve the productivity is called Timetable management.

• Group: a group is a set of students who are timetabled together. For example, classmates.

• Module: a module is a lesson.

• Course: a course is a set of modules for a given group.

• Size of a room: the size of the room is its capacity, its number of seats for students.

Timetables are very important for the university administration. It gives to students and teachers the

right time and the right place to be, the availability of the rooms, the time done by the teachers for the

pay, the availability of the teachers and the students.

2.1.2. What is a “good” timetable?

A timetable encounters two types of constraints, hard constraints and soft constraints. The

constraints considered as hard are the constraints that must be satisfied. These constraints cannot

be violated. Timetable management has a lot of hard constraints. These are some example used in

the research:

• The same module cannot take place twice in a week. A module for a course is unique and

cannot be twice.

• A module cannot take place in a smaller room than required. There will be not enough places

for students and the lesson cannot be given.

• A student cannot be at two places for the same time.

• A place cannot be used for two courses for the same time.

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• A timetable cannot exceed a specific range of days by week, more than 5, from Monday to

Friday. It’s a school time constraint, for administrative tasks for example.

• A module cannot take place in a non-specific place if the required type of place is specified.

• A timetable cannot exceed a specific range of hours by day, and more than 24 hours day.

Theses constraints are human constraints. They are fixed and all timetable witch doesn’t respect it is

not a correct solution.

A soft constraint is a constraint witch may be violated. It should be satisfied for the best but the

timetable is correct even if theses constraints are not satisfied. These are some example used in this

research:

• A module should not take place before a certain hour and after an hour. For example, if the

range is defined from 10AM to 5PM, a course after 5 is possible but it’s not a best solution.

• A group should get a certain number of free days during the week. If this number is defined to

1, a timetable without free day is a solution, a timetable with only 2 of 5 days with courses is

also a solution, but it’s not optimised.

• A group should not get more than a specific number of hours max by day. If it is set to 6, for

example, a day with 7 hours for a group is correct, but it’s not the best solution for the group,

because the group can be exhausted for the last course.

• A group should not get more than a specific number of consecutive hours without break. Same

as before, students of a group will be exhausted if they get 5 hours without break, without

changing the mind.

• A module should not use a place too big or a specific type of place if not needed. Because

some courses need a bigger room, if the room taken is too big, then, it will be a waste of

space, a waste of electricity and waste of heating during winter.

• The distance between places of consecutive modules should not be excessive. This distance

can represent the time that students will take to go from a place to the next place.

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These constraints can be breached. Hard and soft constraints are useful to define what is an

optimized timetable or an incompatible timetable.

2.1.3. What is the actual timetable management problem?

Timetable management system is a complex system used to create a schedule for human tasks.

Bigger is the problem; bigger is the set of solutions. A human can get a result for a small set of

timetable, but if the number of modules to treat gets bigger, no human even actual computers can

create all possibilities.

2.2. Genetic Algorithms

“It’s a procedure inspired in biologic evolution (Charles Darwin), programmed in computers, and

oriented to produce solutions for problems that have been difficult to solve with classical

approaches.”(wgonz 2010)

2.2.1. History

John Henry Holland wrote a book called “Adaptation in Natural and Artificial Systems” in 1975. This

Scientist and Professor of Psychology, Electrical Engineering and Computer sciences improved

Artificial Intelligence field of study giving it a new Algorithm: the Genetic Algorithm, based on the

Darwin theory of evolution to solve problems. He permits to give solutions to problems that classic

algorithms could not solve; using this algorithm he gave a new innovating way to solve Optimisation

problems. Actually, John Holland is considered as one of the father of Artificial Intelligence and the

father of genetic algorithm.

2.2.2. Definitions

Some important definitions:

• Gene: a Gene is a part of solution. It contains some data.

• Chromosome: a Chromosome (or individual) is a solution to the given problem, composed by

genes.

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• Population: set of solutions.

• Soft constraints: constraints that may be breached

• Hard constraints: constraints that must be satisfied

• Generation: a “round” of the algorithm, with a new population, after evolution.

Steps of the GA

Choose initial Population

Repeat

Evaluate population

Select best-evaluated individuals for reproduction

Apply crossover/mutation reproduction

Set new population

Until ending condition (when a individual get the desired evaluation or a specified number

of generations has been done).

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Initiale(population

Evaluation(of(Chromosomes

Selection(of(best(Chromosomes

CrossOver/Mutation

New(Population

Ending(condition

End

Start

2.2.3. Data encoding and decoding

Data encoding is the first step before starting GA. It consists into getting a way to transform a solution

into a chromosome encoding it to get a simple value, like a string. It is used to simplify the treatment

and the improve speed of the algorithm. An easy way to do is to convert the data into a binary string.

A Gene is a part of Chromosome and can be converted to a binary string either. Convert the data to

this type permits an easier treatment for the algorithm. The chromosome string is composed with

side-by-side genes strings.

Encoding

This is a chromosome, or a solution to a problem.

1011100110111010100010100011110100100101101010011010001111

The chromosome can be spited into genes

10111001101110101000 10100011110100100101 10101001101010001111

A gene can be spited to get all the data 10111 0011 011 10101000

23 3 3 168

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Other ways to encode the data are possible; the string representing the chromosome containing the

genes must be maintained. The size of a data into the gene depends of the maximal size of the data

you want to save inside the chromosome. In this example, you can see 0011 for 3, and 011 for 3

also. Theses two values represent two different data. The first one can store data from 0 to 15, the

second one, from 0 to 7. The order of the genes doesn’t alter the solution in many cases. A gene can

be composed of a fixed part and a variable part. The variable part will improve the solution.

2.2.4. Initial Population

Initiate the population is the first step in GA. It consists into creating an amount of random individuals

using hard constraints. The choice of the population depends on the needs of the user. A small

amount of population will get smaller and destroy the full population after some generations because

of evolution can give invalid chromosomes. On opposite a large amount of population will give better

results but will require more resources and will be slower. The population can be represented as a

set.

Population*(set*of*solutions)

2.2.5. Evaluation of the population – Fitness function

Fitness: The fitness of a solution is the estimation of how good the solution is, using soft constraints.

At this range, the solution is valid.

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The evaluation of the population is the heart of the GA. this step estimates which solution is better

than other using a fitness function. The fitness function can give this information by different ways:

The fitness can be given using a range between 0 and 1, where 1 is estimated as the best solution of

the population, using other the other individuals to range them. In this case, there will be always a

solution that the fitness is 1, and a solution that the fitness is 0.

The fitness can be given using a range between 0 and 1, as seen before, but 1 can be a non-

reachable point. The best solution approaches 1. It’s a limit of the perfect solution. This fitness

function depends on a huge number of soft constraints. In this case, there is not a perfect solution,

only best than others.

The fitness can be given using a mark table. Each constraints satisfied give a certain amount of

points, each constraints violated take back an amount of points. This way offers a non-unique

solution, it doesn’t depend on other individuals to gets its fitness and the solution doesn’t get a final

goal.

The fitness function permits to order the population and select the best individuals.


Best*chromosomes

2.2.6. Crossover evolution

The crossover evolution is a method used for the creation of a new population, based on older

population. The simple crossover evolution use two chromosomes and permit to create X new

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chromosomes. It consists into splitting the two chromosomes in parts and creating new

chromosomes using different couple of the parts.

Simple crossover evolution

Chromosome A:

10111001101110101000101000111/10100100101101010011010001111

Chromosome B:

01101110100110110101000101000/11100110110100100100101010011

Crossover generation:

10111001101110101000101000111/01101110100110110101000101000

10111001101110101000101000111/11100110110100100100101010011

01101110100110110101000101000/10100100101101010011010001111

11100110110100100100101010011/10100100101101010011010001111

This crossover solution offers 4 new chromosomes.

Other methods for multiple crossover evolution with more than 2 chromosomes but the treatments

get complex to get all combinations available.

3 chromosomes crossover evolution

Chromosome 1: ABC (simplified chromosome spitted in 3 parts)

Chromosome 2: DEF

Chromosome 3: GHI

Crossover generation: 27 chromosomes: ADG, ADH, ADI, AEG, AEH, AEI, AFG, AFH, AFI, BDG,

BDH, BDI, BEG, BEH, BEI, BFG, BFH, BFI, CDG, CDH, CDI, CEG, CEH, CEI, CFG, CGH, CFI.

But sometimes, the application is trapped because it doesn’t get new best solutions. Crossover

evolution can give to the new population invalid chromosomes or no better ones. At this point, the

algorithm uses mutation to produce new best chromosomes.

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Best*chromosomes

Crossover*solutions

2.2.7. Mutation

Mutation is used to get the algorithm moving. It consists of changing the values of a gene randomly,

permitting an evolution resulting in a new unexpected solution. These solutions offer a new point of

view for the fitness function. The mutation changes only the chromosome, without affecting others

solutions.

Mutation

Original Chromosome:

100111101011 101100101001 101001101010 101111001010

After mutation

100111101011 101010101001 101001101101 101111001010


Best*chromosomes

Crossover*solutions

2.2.8. New population and loop

The crossover and the mutation permit to create a new population of original solutions.

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Evolution

Original two chromosomes:

1011100110111010100010100011111100110110100100100101010011

0110111010011011010100010100010100100101101010011010001111

Application of the crossover method, new population:

1011100110111010100010100011111100110110100100100101010011

0110111010011011010100010100010100100101101010011010001111

1011100110111010100010100011110100100101101010011010001111

0110111010011011010100010100011100110110100100100101010011

1011100110111010100010100011101101110100110110101000101000

1110011011010010010010101001110100100101101010011010001111

At this step, counting initial population, 6 solutions are available. But using Crossover again on these

solutions gives always the same result. The algorithm is looping for infinite. At this step a mutation

on 33% of the population, 2 chromosomes, for example the parents:

1011100110111010100010011011000100110110100100100101010011

0110111001100011010100010100010100100101101011100110001111

This mutation gives two new chromosomes, which can be valid. This gives 8 chromosomes.

With the mutations on the new once, the crossover evolution obtains 4 brand new parts.

The idea of creating a new population of solution based on the older one is to evolve and get a better

population.

The end of the loop can be setup using different conditions:

• The number of the generations can be used as an ending condition. It is useful to see how

powerful the crossover and mutation functions are because of setting a small number of

generations, it can be seen if the changes are slow or fast and give an estimation of the

efficiency of the evolution functions.

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• A fitness condition can be reached and make the end of the loop. Using this way, you can get

a certain range of solution, but the algorithm uses random generation and random mutation. It

can take a long time to change its values and reach the desired goal.

• A limited number of chromosomes can be also used at limit. As the fitness condition, it can

take a long time until you get the result.

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3. Prototyping

This chapter treats about prototyping a Genetic Algorithm for Timetable Management.

3.1. Data stored and encoding

The first step in prototyping a Genetic Algorithm is to define the data stored and how to encode it.

For the timetable management problem, a Chromosome is a set of timetables for different courses,

and a Gene represents a given module. The next array represents the data stored in a gene.

Data Description

Course identifier It permits to know which course this module is part of. For example

the course 1 can represent the third year students modules in

computer science. Because the course represents a set of modules

for a group of students, the number of courses will give a number of

course timetables. This course information is fixed for the module

and may not be changed in the algorithm. This is a fixed data.

Module Identifier A module identifier is used to define the module, and what it is.

Because the gene represents a module, the identifier cannot be

changed.

Duration It represents the length of the module and cannot be changed.

Capacity required It represents the number of students for the module. It’s a fixed

data.

Module Type

Gives information about if the module is a lab or a lecture. It’s a

fixed part of the gene. The course identifier, the module identifier

and the module Type are together the identifier of the gene.

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Room type required Some modules need a specific room, for example, a computer lab

or a network lab. This value is in the fixed part of the gene.

Day of the module It represents which day the module takes place and may vary to

evolve with the genetic algorithm. It’s the variable part.

Hour of the module It represents which hour the module starts and is a variable part

Room number The room number represents where the module takes place. It is a

variable part.

The first try was to encode all the data into a gene, but it started to be too long.

First Gene, with all data encoded:

001010000001110001101100010110010100010011001000

The Gene exploded in data:

001 01000 0001110001 1 0110001011 001 010 001 0011001000

1 8 113 1 395 1 2 1 200

day Hour room course module type duration room type capacity

Gene output:

“It’s a Lecture [1] of 395. This class is on Monday [1] from 8 to 10 [2 Hours] in a Lecture Room [1] in

113.”

The gene is 48 bits long. The variable part (day, hour, room) represents 18bits and the fixed part is

30.

A timetable can contains up to 18 modules by week, the length of the chromosomes is of 864 bits.

The fixed part takes too much place in the chromosome.

The second solution was to combine all the module information into a single index and remove all the

waste of memory.

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Second Gene, with an index instead of all the fixed data:

00101000000111000100000100

The Gene exploded in data:

001 01000 0001110001 00000100

1 8 113 4

day hour room index

This gene represents exactly the same module like the previous example. The size of the gene is

now to 26 bits.

The new gene containing 26 bits, a timetable with 18 modules by week gives chromosomes with a

length of 468 bits.

3.2. Fitness function and loop ending

3.2.1. Constraints

The constraints have to be defined before working on the algorithm. The purpose of this step is to

define how a good timetable is built.

Constraint Type of constraint

Range of day from 1 to 5 (Monday to Friday) Hard constraint

Range of hours per day from 8AM to 7PM Hard constraint

Unity of module in a timetable Hard constraint

No two modules for the same course at same time Hard constraint

A room is for one module at a time Hard constraint

A module cannot take place in a too small room Hard constraint

One day free by week Soft constraint

A range of hours per day from 10AM to 5PM for no courses too late Soft constraint

No more than 6 hours a day Soft constraint

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No more than 3 hours without a break Soft constraint

Some constraints can be set as hard constraints and soft constraints but this is a user choice.

3.2.2. Evaluation of fitness

For this algorithm, a “point table” gives the fitness of each individual in the population.

Constraint Points

Hard constraints Invalid.

One day free by week As many days free, it takes a proportional number of

points to be removed.

A range of hours per day from 10AM to 5PM

for no modules too late.

For each “good” day, it gives an amount of point,

and for each hour out of this range, it takes points by

number of exceeding hours.

No more than 6 hours a day As previously, it gives an amount of points for each

day that respects the constraints.

No more than 3 hours without a break It removes point for each “more than 3” consecutive

hours.

3.2.3. End of loop

The end of the loop and the end of the algorithm is managed by two ways. The first way is to set up a

number of generations. This way offers a good view of how efficient is the genetic algorithm. The

second way to set the end of loop is by using a point value to reach. The algorithm is slow and this

way takes a long time but offer better solutions.

3.3. Conception

Please refer to the class diagram in appendix 3.

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4. Project Management

4.1. The project

As initially started, the project was about a web-application used by lecturers and students to manage

their own timetable, but the project evolved into the field of artificial intelligence, the genetic

algorithms. The project started without any initial knowledge.

4.1.1. Scheduling the project

The first part of the project was dedicated to research about what is a genetic algorithm and how it

works. It was long due to thee less initial knowledge. This part was dedicated to readings.

The second step of the project was the creation of the prototype. This step turned around encoding

the data and creates the fitness function.

Timetable management using genetic algorithms

140 days Starting date

Research 70 days 01/12/2011 Readings about timetable management 20 days 01/12/2011 Readings about genetic Algorithms 50 days 21/12/2011 Prototyping 50 days 09/02/2012 Encoding data 30 days 09/02/2012 Fitness function 10 days 11/03/2012 Mutation & tests 10 days 21/03/2012 Report writing 20 days 01/04/2012

4.1.2. Technology choice

The first technology chose was Java, with the Java Genetic Algorithm Package, the JGAP library. But

a problem has been encountered: The JGAP library was too complex for prototyping. After meeting

with the project supervisor, it has been decided that the project will be done in PHP. PHP is not

specially a language done for algorithm, the application get slower. But the choice has been maid to

simplify the algorithm development. So PHP5 full-object oriented has been decided.

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5. Conclusions

5.1. The prototype

Please refer to the prototype Results in Appendix 4.

The prototyping result offers a good view of the how the timetable is optimised. In appendix 4, we can

see on each algorithm executions the first “best estimated” result and the best timetable chose

between the ten first solutions by the user after a certain number of generations. This shows that

there is an evolution between the original solution and the ending solution. The problem with the

prototype is that the mutation is not using soft constraint to change some chromosomes and gives

only brand new solutions for the solutions mutated.

5.2. Genetic Algorithms

5.2.1. Genetic algorithms opportunities

The genetic algorithms offer ways to solve complex problems of optimisation. They are used in very

famous problems like the travelling salesman problem or the ant colony problem. They offer a huge

set of solutions and give, using Darwin’s theory, a simplified way to treat the problem.

5.2.2. Limits of Genetic Algorithms

The genetic algorithms are limited from multiple sides. The first side of limitation is the time

execution. The Algorithm takes long time to be executed and requires a certain machine

configuration. This can be a problem for time execution.

The second limit of the algorithm is the importance of the random part. Due to a huge set of

solutions, the algorithm cannot guaranty to get the best result or the achievement of a certain level of

fitness. In this case, the algorithm can run indefinably.

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5.3. Personal Conclusion

This project offered me a new field of studies. It was really interesting and deserved all my interest

during the research and the prototyping. I would recommend this project to other students but it

requires good notions about algorithms and a particular interest in maths.

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6. Literature review and bibliography

6.1. Literature review

WGONZ - 2010. Genetic Algorithm – Definition [ONLINE] Available from

<http://www.answermath.com/genetic-algorithms/ga-1-definition.htm> [20 April 2012]

6.2. Bibliography

BAMBRICK (L.) 1997. Lecture Timetabling Using Genetic Algorithms. The University of Queensland BEASLEY (D.), BULL (D.), MARTIN (R.) 1993. An Overview of Genetic Algorithms. Inter-University Committee on Computing DORIGO (M.), GAMBARDELLA (L.M.) 1996. Ant colonies for the traveling salesman problem. Université Libre de Bruxelles HAKAN (Y.) 2000. Simulated Annealing & Applications to Scheduling Problems. Bilkent University HOLLAND (J.) 1975. Adaptation in Natural and Artificial Systems. University of Michigan KOZA (J.) 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Massachusetts Institute of Technology MITCHELL (M.) 1996. An Introduction to Genetic Algorithms. Massachusetts Institute of Technology PRICE (K.), STORN (R.) 1995. Differential Evolution – A simple and efficient adaptive scheme for global optimization over continuous spaces. International Computer Science Institute SCHWARTZ (P.) 2005. Les algorithmes génétiques. Developpez.com WHITLEY (D.) 1994. A Genetic Algorithm Tutorial. Colorado State University WHITLEY (D.) 2001. An Overview of Evolutionary Algorithms. Colorado State University

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7. Appendices

Appendix 1a - project proposal

Coventry University Faculty of Engineering and Computing

Undergraduate Detail Project Specification Template

COM Detail Project Proposal Form Template 09112011 amended.docx Page 1 of 7

FEC Computing Undergraduate Detail Project Specification Form Please type and fill in the following form and all fields. Once completed then submit a electronic copy to Coventry University (see details at the end of this form) and to your supervisor and 2nd marker. Also YOU MUST ensure the ethics procedure has been completed , if not you will not be allowed to continue your project. The fields provided in this template can be extended to accommodate your text.

Details

Surname (Family Name )

Larget Forename(s) (Given Name)

Henri

Student ID no.

3913651 Course

ECU098 – BSc Informatics

Contact E-Mail

[email protected] Mobile/Contact

No.

07 774 960 086

First Supervisor Faiyaz Doctor

2nd Marker Brian Moore

Project Details General Subject Area Keywords /Topic areas listed (A broad topic area that defines the area of project and keywords that could be used in a search facility) Timetable, management, web, application, easy-to-use Project Title : A proposed title of the project - meaningful, succinct and clear as to the project undertaken. Timetable manager for students Abstract/Summary : Please provide some background about the project, topic, company, situation and overall summary as to the nature of problem and what is required (approx 500 words) Coventry is a university for students from everywhere. And lot of students don’t speak great English when they arrive. They get lost really fast if they miss something. The first week, lecturers gave them a lot of information, and about the fact that they have to create their own timetable depending courses. But information about courses, timetables and subscriptions are in 3 separated tools. Some teacher prefer to give information by mail, other through Moodle tool, and someone by the special website. I was in this case, it’s why I choose this project.




The idea of this project is to create a timetable management tool suitable to the needs of students and lecturers. The first part of this project is to ask people what they exactly expect from the system, and analyse user needs. It can be done by conducting a survey, for lecturers and students prepared using Moodle tools or google documents tools for example. This is, the more efficient way to do it, because the survey will cover more people than by going door to door asking people. The second step of the project is proper preparation before designing the application. It’s an important part of the application to do schematics and preparation. It is needed to know exactly what the application needs, the database design and after that, the implementation The developing part is the longest part, not the hardest one, but the longest. The application will be designed with PHP5 language in with a design pattern to keep a code able to be reused and expanded, using a MySQL database, and JavaScript for user interaction. The written code will be documented to help next users to continue the project. The application should be able to do the following tasks successfully: A Lecturer will be able to create a course and get to see who subscribed to his course. Students will be able to learn more about their courses. They will be able to subscribe, if there is enough place, and check hours to avoid conflicts. The system will generate a coloured view of a timetable. Lecturers will be able to see how many people subscribed to the course and contact all of them by mail. It offers an easy way to manage the Solar, the information and the timetable tools in one. Last part is the testing and documents writing part of the project. The testing part is one of the most important. In fact, A group of students and teachers will be assigned to test the application. The idea of this part is to try to simulated a situation and see how they the test-group will react, to get if the application answers to the initial survey. The final report will create the link between the initial survey and the test of the application by the test-group composed by students and teachers. Aim and Objectives Aim – what are we trying to achieve overall and the hypothesis being tested. List overall 7-10 objectives of the project. These should be measurable where possible and will be used in part assessment in determining the level of achievement of the project. These should reflect the Learning outcomes of the Module. (Can be a breakdown of what you are trying to achieve and can be representative of activities/ phases that are to be conducted in arriving at the project deliverables) Aim Simplify Academic life with a timetable tool created in collaboration with students and teachers. Hypothesis Can we create a tool designed to fit to everyone with only consulting a sample




group? How to simplify the creation of timetables for everyone with only a sample group? Objectives (7-10)

- Recording data, analyse and reuse it. - Maintain System development life cycle. - Create application - Manage project - Permit the application to be extended - Test-group organisation - Test-group analysis

Project Outcomes/ Deliverables : Provide a list of key outcomes of the conducted project (can be deliverables/outputs for each of the above objectives/ stages and these can be studies, reports, requirements, designs, plans, coding, recommendations etc )

- Report: survey results and analysis - Report: Project Management analysis - Code: the application - Documentation: user guide and technical guide - Report : Test group organisation and results - Report: final report

Motivation - Why are you interested in the project ? Provide a reason for your interest, what greater general interest it serves (In Industry or as part of Research) and who it could benefit (the target audience(s) that this project would be beneficial to) Motivation This project is the first one I start from beginning to ending alone. When I arrived in Coventry in September, my English were not excellent and asking people help for timetables were complicated. I want to develop a system to help people from everywhere to manage their timetable. I propose the project to initiate university to change the system and get a more accessible timetable. Targets for this application are students and teachers from Coventry university. Client Details and extend of involvement




What research methods do you intend to use to achieve your objectives / produce your deliverables? (Discuss the nature of investigation considering the multitude of academic and Industry methods, with thoughts on qualitative and quantitative methods, and primary and secondary methods) Detail (research or procedural) methods that you will use Research Methods First part of the research is provided by the survey. It consists in creating a survey using Google documents or Moodle and collect answers. The analysis of those answers will provide help to create the application. The second part of the research is conduced by test-group working. In fact, application will be tested with a specific situation. The test group will simulate actions on the application and the results will be get by survey. Quantitative:

- The survey - The test group survey

What primary and/or secondary data sources do you intend to use? (for each of the above identified methods discuss the pool of sources of authentic and credible information giving examples, e.g. consider target audiences such as profiles of general public, company representatives, people with specialist knowledge and in specific positions, type and title of books, journals etc) Technologies used:

- PHP5 for server-side view - MySQL for the database - JavaScript, HTML and CSS for the client-side view - Apache Server for web developing

Software used:

- Netbeans for development - Adobe Illustrator for graphics

Data sources :

- Web - Students’ and lecturers’ survey




Project Schedule Attach/ Embed a project schedule to this document (this can be a Gantt chart or any form of representation – MS project, excel, PowerPoint etc) which incorporates the phases of your project and activities to undertake, duration, start and end dates, any milestones/ deliverables and major dependencies.




Return of form to Coventry University

Thank you for completing the form please ensure your supervisor and assessor has a copy ALSO submit a paper copy to the assessments office by the deadline as well as submitting an electronic copy by Moodle by the deadline. (Deadline is 28th November 2011) Tariq Aslam, Undergrad project coordinator




Marking sheet for detailed Project proposal, this constitutes 10% of your project marks

Degree of challenge vs LO’s Level of completeness >70% - clearly considered project that fully

satisfies the Learning outcomes for which there is a succinct and focused aim with an associated project question/hypothesis that is well above norm for final-year undergraduate project level (approaching Masters level for >80%); -Project involves improving or developing a complex programme /tool/ application or the enhancement of a theory/methodology or their application in new settings. Demonstration of innovation / creativity

- All fields completed demonstrating a clear blueprint for the research process and includes the necessary information with respect to the research dilemma - research methods are well-considered with clear reasoning for choice of those methods over others; -Appropriate level of detail in project schedule including realistic timescales that will allow student to successfully manage their own project and strongly linked to objectives, deliverables and research methods;

Threshold (40%)

-Fairly standard project with some consideration to a greater context. -idea which lacks substance, context and scope for depth of analysis, but which is marginally acceptable against a threshold for final-year undergraduate projects;

- Completion of sections though very minimal with some cohesiveness and contextualisation -Sections demonstrating some understanding of the research process involved which loosely links with idea outlined (aim, key question/hypothesis, objectives); - Research methods are discussed but are obvious with little consideration as to whether they are the most appropriate and lack refinement and further detail. - Not all major tasks identified in project schedule with inappropriate timings (overly optimistic or generic);

MAX MARKS Degree of challenge and viability of project with respect to learning outcomes (see Below)

5

Level of completeness of proposal and all fields completed with project schedule

5

26

Appendix 1b - project proposal marking

Marking sheet for detailed Project proposal for UG Dept of computing final year

Degree of challenge vs LO’s Level of completeness

>70% - clearly considered project that fully satisfies the Learning outcomes for which there is a succinct and focused aim with an associated project question/hypothesis that is well above norm for final-year undergraduate project level (approaching Masters level for >80%); -Project involves improving or developing a complex programme /tool/ application or the enhancement of a theory/methodology or their application in new settings. Demonstration of innovation / creativity

- All fields completed demonstrating a clear blueprint for the research process and includes the necessary information with respect to the research dilemma - research methods are well-considered with clear reasoning for choice of those methods over others; -Appropriate level of detail in project schedule including realistic timescales that will allow student to successfully manage their own project and strongly linked to objectives, deliverables and research methods;

Threshold (40%)

-Fairly standard project with some consideration to a greater context. -idea which lacks substance, context and scope for depth of analysis, but which is marginally acceptable against a threshold for final-year undergraduate projects;

- Completion of sections though very minimal with some cohesiveness and contextualisation -Sections demonstrating some understanding of the research process involved which loosely links with idea outlined (aim, key question/hypothesis, objectives); - Research methods are discussed but are obvious with little consideration as to whether they are the most appropriate and lack refinement and further detail. - Not all major tasks identified in project schedule with inappropriate timings (overly optimistic or generic);

Learning Outcomes

1. Devise, perform and take responsibility for a self-managed project within an appropriate ethical framework.

2. Research and compile information relevant to the specialist subject area in which the project is based, maintaining a suitable log for recording information obtained and progress/developments in the project.

3. Undertake analysis, design, practical work and testing as required to achieve the agreed objectives of the project.

4. Critically evaluate the achievements of a project, draw appropriate conclusions and suggest areas for further work.

5. Communicate the work done, achievements attained and conclusions reached through both oral and written means

MAX MARKS Degree of challenge and viability of project with respect to learning outcomes (see Below)

5

Level of completeness of proposal and all fields completed with project schedule

5

Coursework Assessment Feedback form for UG project Proposal specification

Surname (Family Name )

LARGET Forename(s) (Given Name)

HENRI

Student ID no.

3913651 Course

BSc Informatics

Module Code

300COM 303COM

Project Proposal specification (10% of module mark)

Please provide Feedback Marks

Degree Of challenge (50%)

Reasonable for UG project however addition of a research and investigative element would improve the project.

3/5

Level/ Degree of completeness of proposal (50%)

All sections completed but needs better clarity and definitions of research methods functional objectives of the system with more specific objects.

3/5

Key Areas of strength of project /proposal

Practical useful project to serve students and lectures at university. The project also has potential to address research questions on timetabling and scheduling issues.

Key areas of improvement of project /proposal

Project could be extended to include timetable optimisation based on investigation and possible application of constraint satisfaction algorithms. Hypothesis/ project question seems a little vague and not fully considered, needs to be a more clear defining possibly a test question that the system would answer. Project objects are not specific enough need to be more strongly linked to aims. Motivation also needs to be more specific specifying what general and research purposes this project would serve. What software design mythology is used.

Mark by supervisor :

6/10

Marker : Faiyaz Doctor

Final Mark :

Moderated Mark (if applicable)

Moderator : Date :

/10

Module Leader Signature

27

Appendix 2 - PowerPoint presentation

26/04/12'

1'

3RD YEAR PROJECT HENRI LARGET

PRESENTATION ON 5 APRIL 2012 COVENTRY UNIVERSITY – UNITED KINGDOM

THE PROJECT •  What is a « good » timetable?

•  How to select a solution between a huge population of Solutions?

Genetic Algorithms used in Timetable Management

AIMS AND OBJECTIVES •  Aims :

•  Understand the difficulty of timetable management •  Starting on a solution : Genetic Algorithms

•  Get a better understanding of genetic algorithms •  Create a list of Genetic sontraints •  Design a prototype of Genetic Algorithm for timetable

PROJECT MANAGEMENT : STEPS OF THE PROJECT •  Get a better understanding of timetable management and

genetic Algorithms

•  Define a encoding system

•  Define « hard constraints » and « soft constraints » of the timetable

•  Develop a prototype of the algorithm

•  Tests phases with the constraints

PRIMARY RESEARCH APPROACH – TIMETABLE MANAGEMENT •  Some Problems with timetable management :

•  Hard to calculate because of the constraints •  Hard to manually calculate when the problem becomes

bigger

Verify integrity of population

Evaluate Population •  Select part of the

population

CrossOver •  Cross the selected

population

Mutation •  Change content of

the chromosomes

What is a genetic algorithm ?

How does it work?

PRIMARY RESEARCH APPROACH – GENETIC ALGORITHMS

26/04/12'

2'

DEMONSTRATION

RESULTS (AFTER ONLY 20 ROUNDS)

CONCLUSION •  Timetable problems

•  A lot of human constraints

•  Genetic Algorithms for timetable

•  Powerful for the problem •  But really slow

BIBLIOGRAPHY •  Leon Bambrick, Lecture Timetabling Using Genetic Algorithms - University of Queensland

•  David Beasley, David R. Bull, Ralph R. Martin, An Overview of Genetic Algorithms - 1993

•  John R. Koza, Genetic Programming as a Means for programming computers by natural Selection –Stanford University

•  Pierre Schwartz – les Algorithmes génétiques – 2005

•  Mitchell Melanie – An Introduction to Genetic Algorithms – 1996 - ISBN 0−262−13316−4 (HB), 0−262−63185−7 (PB)

28

Appendix 3 - class diagram

GeneticAlgorithm

population : arraypreviousScore : integerelite_size : integer

algorithm(round)

initiatePopulation(numberO

fUnits)orderByConstraints()selectTheBests()crossover()m

utate()

Chromosom

e

genes : arrayroom

Constraint : RoomConstraint

courseConstraint : CourseConstraint

addGene(gene)random

ize()random

izeWithM

utation()resetConstraints()getPoints()isCorrect()

Gene

day : integerhour : integerroom

: integerm

odule : integer

encode(value,size)decode(binary)

Constraint

DAY_M

IN : Integer

DAY_M

AX : IntegerH

OUR_M

IN : integer

HO

UR_MAX : Integer

COURSE_M

IN : Integer

COURSE_M

AX : IntegerH

OUR_LIM

IT_MIN

: IntegerH

OUR_LIM

IT_MAX : Integer

NUM

BER_FREE_DAYS : Integer

NUM

BER_HO

URS_MAX_BY_D

AY : IntegerN

UMBER_SUCCESSIVE_H

OURS : Integer

Timetable

matrix : array

initialize()isBusy(id,day,hour,duration)setBusy(id,day,hour,duration)

Courses

getEarlisetHours(course,day)

getLatestHours(course,day)

getNum

berOfFreeD

aysUsingCourses(course)getN

umberO

fHoursAD

ay(course,day)getN

umberO

fHoursConsecutives(course,day)

checkAndAdd(course,day,hour,duration)

Rooms

rooms : array

getRoom(size,day,hour,duration,m

oduleType)

Modules

modules : Array

initialize()

Module

courseID : int

moduleID

: Stringtim

eDuration : int

roomCapacity : int

moduleType : int

roomType : int

29

Appendix 4 - Prototype results

Report 1 Initial population: 300 Chromosomes Number of rounds: 20 Generation 0: Score: 491

Generation 20: Score: 511

Report 2 Initial population: 300 Chromosomes Number of rounds: 20 Generation 0: Score:504

Generation 20: Score: 518

2012 - h larget - genetic algorithms used in timetable management

Documents