Summary of Evolutionary Computing

Summary of Evolutionary Computing

Overview Last two weeks we looked at evolutionary algorithms.

Overview This week we are going summaries these into: • Basic Principles • Applications

Basic Principles 1: Overview

Basic Principles 2: Population • A population of individual possible solutions to a particular problem.

Basic Principles 2: Population • Each individual (or chromosome) encodes the solution.

Basic Principles 2: Population • Each individual needs to evaluated.

Basic Principles 2: Population • Example encoding include: • Binary representations • Real valued representation • Integers for order based representations.

Basic Principles 3: Reproduction • Parents are selected randomly • Better/fitter individual - more likely it is to selected. • Fitness - evaluation individuals

Basic Principles 3: Reproduction • Child produced takes something from both parents.

Basic Principles 3: Reproduction • Different methods of selection are available.

Basic Principles 4: Selection methods: Roulette Wheel • Illustration taken from www2.cs.uh.edu/~ceick/ai/EC1.ppt Fitter the solution -more space on the wheel -more likely to be selected Best Worst

Basic Principles 5: Crossover • x amount of ‘genes’ from one parent is included in the child and y amount from the other parent is included.

Basic Principles 5: Crossover • One way to do this is to say: certain point along the chromosome copy • Up to this point from one parent • After this point from the other parent.

Crossover causes ‘good’ individuals to combine their ‘genes’ with those of other individuals.

Goal - population of ‘good’ solutions.

combination of different solutions.

speeds up search –average fitness of the population improves rapidly at first.

Basic Principles 6: Mutation • Mutation causes random selected changes to an individual.

Basic Principles 6: Mutation • Often random valued changes

Basic Principles 6: Mutation • Binary: 11000110 becoming 11010110

Basic Principles 6: Mutation • Real: 2.3 3.4 5.6 becomes 2.3 5.4 5.6

Basic Principles 6: Mutation • Low probability event

Basic Principles 6: Mutation • Get the population to include different individual solutions.

Basic Principles 7: Fitness • Every individual needs to be evaluated – fitness score.

Basic Principles 7: Fitness • This evaluation is usually in the form of function.

Basic Principles 7: Fitness • Examples include: • The equation to be solved. • Differences between actual and expected results.

Basic Principles 7: Fitness • The only link between the possible solutions and effectiveness to solve the problem.

Basic Principles 8: Population Size. • Need to decide how the population size to managed: • Fixed size, maintained by every child added a previous solution is deleted.

Basic Principles 8: Population Size. • Add child without removing individuals? • Replace a small number of individuals each time or the whole population?

Basic Principles 8: Population Size. • Best solution(s) kept in the population – elitism.

Applications 1: Financial/Scheduling • Stock market: • http://www.geocities.com/francorbusetti/mansini.pdf • http://www.geocities.com/francorbusetti/gillikellezi.pdf • Scheduling examples • http://www.aridolan.com/ofiles/ga/gaa/TspDemo.aspx

Applications 2: Engineering • Assembly • http://www.nait.org/jit/Articles/chen080301.pdf • Biomedical • http://www.journals.elsevierhealth.com/periodicals/jjbe/article/PIIS1350453303000213/abstract

Summary of Evolutionary Computing

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