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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

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

  4. Optimised Man! CS 561

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

  9. A “Population” CS 561

  10. Ranking by Fitness: CS 561

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

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

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

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

  15. Best Design CS 561

  16. The GA Cycle CS 561

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

  19. Typical “Chromosome” CS 561

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