Theoretical analysis of mutation-adaptive evolutionary algorithms.
Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting for a complete, rather than recent, history of the algorithm's evolution. Under the new paradigm, we analyze the convergence of several mutation-adaptive algorithms: a binary genetic algorithm, the 1/5 success rule evolution strategy, a continuous, respectively a dynamic (1+1) evolutionary algorithm.
Agapie
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Laboratory of Computational Intelligence, Institute for Microtechnologies, Bucharest, P.O. Box 38-160, 72225, Romania. agapie@rdslink.ro
D000465:Algorithms; D005075:Biological Evolution; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation; D013269:Stochastic Processes