1. Genetic Algorithms K.Ganesh Reasearch Scholar, Ph.D., Industrial Management Division, Humanities and Social Sciences Department, Indian Institute of Technology Madras, Chennai,TN,India.

2. Overview Introduction Introduction History History Details Details Example Example Future Future

3. Introduction Optimization has for long been an important goal in the fields of Applied Mathematics and Computer Science. As the name suggests this method is based on Darwin’s Theory of evolution. Genetic algorithms arose from computer simulations of biological evolution in the late 60s and early 70s. Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence

4. History 1960 – Introduced by I. Rechenberg 1960 – Introduced by I. Rechenberg 1975 – Popularized by John Holland 1975 – Popularized by John Holland 1975 - book "Adaptation in Natural and Artificial Systems" published 1975 - book "Adaptation in Natural and Artificial Systems" published 1992 – John Koza’s work 1992 – John Koza’s work

5. Details A description of the biological terms used: 1. Chromosomes 1. Genes 2. Locus 2. Reproduction 1. Crossover 2. Mutation

6. 1. Start 2. Fitness 3. New Population 1. Selection 2. Crossover 3. Mutation 4. Accepting 4. Replace 5. Test 6. Loop General Outline

7. 1. Encoding Chromosome 11101100100110110 Chromosome 21101111000011110 2. Fitness & Selection The fitness function f(x) Associates fitness of a chromosome to a single number This number determines the chance of selection for reproduction

8. 4. Mutation Original offspring 11101111000011110 Original offspring 21101100100110110 Mutated offspring 11100111000011110 Mutated offspring 21101101100110110 Chromosome 111011 | 00100110110 Chromosome 211011 | 11000011110 Offspring 111011 | 11000011110 Offspring 211011 | 00100110110 3. Crossover

9. Example Character Evolution Algorithm Character Evolution Algorithm 1.Generate some random individuals 2.Select the n best of them depending on their fitness 3.Take those n best to produce some new individuals, based on the information they hold. Repeat from step 2, until you reach what you want. Fitness Fitness Reproduction & Cumulative Selection Reproduction & Cumulative Selection Mutation Mutation View Applet View Applet View Applet View Applet

10. Applications Decision Making Decision Making Data Mining Data Mining Scheduling Scheduling Computer games Computer games Stock Market Trading Stock Market Trading Medical Medical Information Systems Applications Information Systems Applications Finance Applications Finance Applications

11. References Cawsey, Allison. The essence of A.I.. Prentice Hall. 1998 Cawsey, Allison. The essence of A.I.. Prentice Hall. 1998 Introduction to Genetic Algorithms. Introduction to Genetic Algorithms. http://cs.felk.cvut.cz/~xobitko/ga/ Applications of Genetic Algorithms. http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol1/tcw2/article1.html Applications of Genetic Algorithms. http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol1/tcw2/article1.html http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol1/tcw2/article1.html Genetic Algorithms Genetic Algorithms http://http1.brunel.ac.uk:8080/depts/AI/alife/ga.htm http://http1.brunel.ac.uk:8080/depts/AI/alife/ga.htmhttp://http1.brunel.ac.uk:8080/depts/AI/alife/ga.htm Evolutionary Algorithms http://www2.informatik.uni-erlangen.de/IMMD- II/Persons/jacob/Evolvica/Java/CharacterEvolution/index.html Evolutionary Algorithms http://www2.informatik.uni-erlangen.de/IMMD- II/Persons/jacob/Evolvica/Java/CharacterEvolution/index.htmlhttp://www2.informatik.uni-erlangen.de/IMMD- II/Persons/jacob/Evolvica/Java/CharacterEvolution/index.htmlhttp://www2.informatik.uni-erlangen.de/IMMD- II/Persons/jacob/Evolvica/Java/CharacterEvolution/index.html Genetic Algorithm, Ashish Gupta. Genetic Algorithm, Ashish Gupta. Genetic Algorithm and Classifier System, David Goldstein. Genetic Algorithm and Classifier System, David Goldstein.