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Genetic Search Algorithms Matt Herbster Why Another Search? Designed in the 1950s, heavily implemented under John Holland (1970s) Genetic search is intended to simulate natural systems Works best on continuous and discrete combinatorial problems It will only take eight minutes of your time Definitions Chromosome Gene Allele Locus Genotype Phenotype String Feature, character Feature value String position Structure Parameter set Characteristics Reproduction Crossover Mutation Rarely used Mutation Operations Generative Swap Sequence Swap Node Destructive Crossover Operations Order based Single point Other Representations Array, matrix Tree String of bits Any other data structure Implementation 1. Start with an initial gene pool 2. Generate successors (either randomly or deterministically) to create the first generation pool 3. Each node is evaluated by a fitness function and sorted accordingly 4. Create new generations with the better most likely to reproduce What is the meaning of the word better? As determined by fitness function (essentially a heuristic) Nodes with desired genes are predetermined Can often approach local maxima rather than the global optimal solution Assisted by random-restart hill climbing Variations Genetic algorithms produce optimal results for many problems, eventually … Speciation – Two nodes will reproduce only if closely related Technique helps improve speed Parallel populations – simulates physical separation with possible migration Applications Traveling salesman problem Drilling of printed circuit boards Planning bus routes Scheduling Computer games – represent an evolution of players’ strategies Stock market trading – data fitting, trend spotting, budgeting Questions