summary of evolutionary computing
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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. - PowerPoint PPT PresentationTRANSCRIPT
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 beselected
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: FitnessEvery individual needs to be evaluated – fitness score.
Basic Principles 7: FitnessThis evaluation is usually in the form of function.
Basic Principles 7: FitnessExamples include:
◦The equation to be solved.
◦Differences between actual and expected results.
Basic Principles 7: FitnessThe 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/Ts
pDemo.aspx
Applications 2: Engineering Assembly
http://www.nait.org/jit/Articles/chen080301.pdf
Biomedical http://www.journals.elsevierhealth.com/p
eriodicals/jjbe/article/PIIS1350453303000213/abstract