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Genetic Algorithms

Explore traditional genetic algorithms - a tried and true approach for optimization. Learn how to adapt existing designs, consult experts, and navigate through trial and error. Discover Nature's starting point and Alison Everitt’s insights in "A User's Guide to Men." Compare traditional methods with the genetic algorithm approach, which efficiently finds global optima in minutes. Understand the genetic algorithm process: defining parameters, generating populations, assessing fitness, mate selection, crossover, and mutation. Rank designs by fitness, select mates through strategies like roulette selection, perform crossover to exchange information, and introduce mutations to avoid local minima. Witness the benefits and drawbacks of genetic algorithms, which excel in finding optimal solutions within a region but may be slower in pinpointing the absolute best solution. Delve into genetic approaches, encompassing genetic algorithms (GA) for genes and genetic programming (GP) for executable programs.

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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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