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Evolutionary Computation

Summer 2001, Vol. 9, No. 2, Pages 127-146
(doi: 10.1162/106365601750190370)
© 2001 Massachusetts Institute of Technology
Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms
Article PDF (250.32 KB)

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.