Theoretical analysis of mutation-adaptive evolutionary algorithms.

A Agapie

doi:10.1162/106365601750190370

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Theoretical analysis of mutation-adaptive evolutionary algorithms.

A Agapie

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Abstract

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.

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Algorithms Biological Evolution Markov Chains Models, Genetic Models, Statistical Mutation Stochastic Processes Journal Article Research Support, Non-U.S. Gov't

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A Agapie. 2001. Theoretical analysis of mutation-adaptive evolutionary algorithms.. https://doi.org/10.1162/106365601750190370

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