1. 1 Genetic Algorithms

2. CS 561, Session 26 2 The Traditional Approach Ask an expert Adapt existing designs Trial and error

3. CS 561, Session 26 3 Nature’s Starting Point Alison Everitt’s “A User’s Guide to Men”

4. CS 561, Session 26 4 Optimised Man!

5. CS 561, Session 26 5 Example: Pursuit and Evasion Using NNs and Genetic algorithm 0 learning 200 tries 999 tries

6. CS 561, Session 26 6 Comparisons Traditional best guess may lead to local, not global optimum Nature population of guesses more likely to find a better solution

7. CS 561, Session 26 7 More Comparisons Nature not very efficient at least a 20 year wait between generations not all mating combinations possible Genetic algorithm efficient and fast optimization complete in a matter of minutes mating combinations governed only by “fitness”

8. CS 561, Session 26 8 The Genetic Algorithm Approach Define limits of variable parameters Generate a random population of designs Assess “fitness” of designs Mate selection Crossover Mutation Reassess fitness of new population

9. CS 561, Session 26 9 A “Population”

10. CS 561, Session 26 10 Ranking by Fitness:

11. CS 561, Session 26 11 Mate Selection: Fittest are copied and replaced less-fit

12. CS 561, Session 26 12 Mate Selection Roulette: Increasing the likelihood but not guaranteeing the fittest reproduction

13. CS 561, Session 26 13 Crossover: Exchanging information through some part of information (representation)

14. CS 561, Session 26 14 Mutation: Random change of binary digits from 0 to 1 and vice versa (to avoid local minima)

15. CS 561, Session 26 15 Best Design

16. CS 561, Session 26 16 The GA Cycle

17. CS 561, Session 26 17 Genetic Algorithms Adv: Good to find a region of solution including the optimal solution. But slow in giving the optimal solution

18. CS 561, Session 26 18 Genetic Approach When applied to strings of genes, the approaches are classified as genetic algorithms (GA) When applied to pieces of executable programs, the approaches are classified as genetic programming (GP) GP operates at a higher level of abstraction than GA

19. CS 561, Session 26 19 Example: Karl Sim’s creatures Creatures Sea Horse Snake

20. CS 561, Session 26 20 Typical “Chromosome”