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Valery Frolov
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The algorithm Fitness function Crossover Mutation Elite individuals Reverse mutations Some statistics Run examples
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The most standard evolution algorithm While (condition isn’t met) Select fittest individuals Crossover Mutation Update population The condition Simulation time
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Ideal crossover function for our problem Learns the relation between different parameters Performs the crossover accordingly My crossover Interleaves two parents with equal probability Creates one sibling
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Each new population consists of Elite individuals from previous population Individuals created by crossover Several random individuals
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Each population consists of K individuals Best M individuals are copied to the next population. M is 1/3 of population size Crossovers are done with at least one elite parent
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Problem : Mutations can make things worse Elite individual that survived many generation can be removed because of one bad mutation Our solution : Reverse previous mutation if condition is satisfied
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Size of population is 16 Time per each individual is 4450 ticks Individuals get bonus points for surviving Individuals get penalty for non-completed laps
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In most cases starting population consists of pair of “good” and many “bad” individuals At least half of final generation are “very good” individuals
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