Convergence Rates For The Distribution Of Program Outputs
نویسنده
چکیده
Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain convergence theorems give general upper bounds on the linear program sizes needed for convergence. Tight bounds (exponential in N , N logN and smaller) are given for five computer models (any, average, cyclic, bit flip and Boolean). Mutation randomizes a genetic algorithm population in 1 4 (l + 1)(log(l) + 4) generations. Results for a genetic programming (GP) like model are confirmed by experiment.
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