Theoretical analysis of mutation-adaptive evolutionary algorithms
Adaptive evolutionary algorithms require a more sophisticated modeling than their static-para meter 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.
evolution strategies genetic algorithms markov chain convergence in probability dependence with complete connections ergodic behavior for a stochastic process