Valery Frolov

 The algorithm  Fitness function  Crossover  Mutation  Elite individuals  Reverse mutations  Some statistics  Run examples

 The most standard evolution algorithm  While (condition isn’t met)  Select fittest individuals  Crossover  Mutation  Update population  The condition  Simulation time

 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

 Each new population consists of  Elite individuals from previous population  Individuals created by crossover  Several random individuals

 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

 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

 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

 In most cases starting population consists of pair of “good” and many “bad” individuals  At least half of final generation are “very good” individuals