2. Overview Last two weeks we looked at evolutionary algorithms.

3. Overview This week we are going summaries these into:  Basic Principles  Applications

4. Basic Principles 1: Overview

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

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

7. Basic Principles 2: Population  Each individual needs to evaluated.

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

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

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

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

12. 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

13. 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.

14. 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.

15. Children 1111000000001111 Parents (crossover point at half way along sequence) 0000 1111

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

17. Goal - population of ‘good’ solutions.

18. combination of different solutions.

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

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

21. Basic Principles 6: Mutation  Often random valued changes

22. Basic Principles 6: Mutation  Binary: 11000110 becoming 11010110

23. Basic Principles 6: Mutation  Real: 2.3 3.4 5.6 becomes 2.3 5.4 5.6

24. Basic Principles 6: Mutation  Low probability event

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

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

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

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

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

30. 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.

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

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

33. Applications 1: Financial/Scheduling  Stock market:  http://www.geocities.com/francorbusetti/mansini. pdf http://www.geocities.com/francorbusetti/mansini. pdf  http://www.geocities.com/francorbusetti/gillikelle zi.pdf http://www.geocities.com/francorbusetti/gillikelle zi.pdf  Scheduling examples  http://www.aridolan.com/ofiles/ga/gaa/TspDemo. aspx http://www.aridolan.com/ofiles/ga/gaa/TspDemo. aspx

34. Applications 2: Engineering  Assembly  http://www.nait.org/jit/Articles/chen080301.pdf http://www.nait.org/jit/Articles/chen080301.pdf  Biomedical  http://www.journals.elsevierhealth.com/periodical s/jjbe/article/PIIS1350453303000213/abstract http://www.journals.elsevierhealth.com/periodical s/jjbe/article/PIIS1350453303000213/abstract