Theoretical analysis of mutation-adaptive evolutionary algorithms

• Published in: Evolutionary computation Vol. 9; no. 2; p. 127
• Main Author: Agapie, A
• Format: Journal Article
• Language: English
• Published: United States 01-06-2001
• Subjects: Algorithms; Biological Evolution; Markov Chains; Models, Genetic; Models, Statistical; Mutation; Stochastic Processes
• ISSN: 1063-6560
• DOI: 10.1162/106365601750190370

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.