1. 1 Genetic Algorithms

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

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

4. CS 561 4 Optimised Man!

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

6. CS 561 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 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 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 9 A “Population”

10. CS 561 10 Ranking by Fitness:

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

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

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

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

15. CS 561 15 Best Design

16. CS 561 16 The GA Cycle

17. CS 561 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 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 19 Typical “Chromosome”