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.

Children Parents (crossover point at half way along sequence)

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: becoming

Basic Principles 6: Mutation  Real: becomes

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:  pdf pdf  zi.pdf zi.pdf  Scheduling examples  aspx aspx

Applications 2: Engineering  Assembly   Biomedical  s/jjbe/article/PIIS /abstract s/jjbe/article/PIIS /abstract