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Genetic Algorithms

Outline. Quick Overview of GAsPseudocodeExample Simple ApplicationsIntroductory ExamplesMusicGA for GAs?Genetic ProgrammingSetting up a GATraveling Salesman Problem. GA Pseudocode. pop = init_random_population(size)while avg_fitness(pop) < fit_threshold: pop = select_good_ones(pop)

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Genetic Algorithms

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    1. Genetic Algorithms Matt Whitehead Sept. 13, 2005

    2. Outline Quick Overview of GAs Pseudocode Example Simple Applications Introductory Examples Music GA for GAs? Genetic Programming Setting up a GA Traveling Salesman Problem

    3. GA Pseudocode pop = init_random_population(size) while avg_fitness(pop) < fit_threshold: pop = select_good_ones(pop) pop = crossovers(pop) pop = mutations(pop)

    4. Quick Overview of GAs Optimization technique Some set of parameters that needs to be optimized The key element: Fitness Function How is fitness determined? Defining an appropriate fitness function is essential

    5. Show Simple Examples

    6. Genetic Programming Grow your own programs Programs are represented as trees of instructions (Think Lisp or Scheme) Crossover is performed by two programs trading subtrees Mutation is the alteration of a single instruction or value to another

    8. Traveling Salesman Classic NP-hard problem Problem statement: Given the distances between each pair of cities in a set, what is the fastest way to visit each city once and return to the starting city? Applications shipping goods by plane, truck, etc. movement of troops er, traveling salesmen

    10. How would you use GAs to solve this problem? What would/could chromosomes in the population look like? (How is the information encoded?) What would the fitness function be like? Would a GA have any problems with your setup? Repeated cities, relative fitness rankings, etc.

    11. References GP image: http://sawww.epfl.ch/SIC/SA/publications/SCR95/7-95-24a.html Music: http://www.lilypond.org/web TSP: http://www.tsp.gatech.edu/ GP: http://www.genetic-programming.com

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