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Genetic Algorithms

Genetic Algorithms. The Traditional Approach. Ask an expert Adapt existing designs Trial and error. Nature’s Starting Point. Alison Everitt’s “A User’s Guide to Men”. Optimised Man!. Example: Pursuit and Evasion. Using NNs and Genetic algorithm 0 learning 200 tries 999 tries.

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Genetic Algorithms

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  1. Genetic Algorithms

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

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

  4. Optimised Man! CS 561, Session 26

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

  9. A “Population” CS 561, Session 26

  10. Ranking by Fitness: CS 561, Session 26

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

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

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

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

  15. Best Design CS 561, Session 26

  16. The GA Cycle 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 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 CS 561, Session 26

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

  20. Typical “Chromosome” CS 561, Session 26

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