CMA-ES with Two-Point Step-Size Adaptation

We combine a refined version of two-point step-size adaptation with the covariance matrix adaptation evolution strategy (CMA-ES). Additionally, we suggest polished formulae for the learning rate of the covariance matrix and the recombination weights. In contrast to cumulative step-size adaptation or to the 1/5-th success rule, the refined two-point adaptation (TPA) does not rely on any internal model of optimality. In contrast to conventional self-adaptation, the TPA will achieve a better target step-size in particular with large populations. The disadvantage of TPA is that it relies on two additional objective function evaluations. Key-words: optimization, evolutionary algorithms, covariance matrix adaptation, step-size control, self-adaptation, two-point adaptation ∗ Adaptive Combinatorial Search Team, Microsoft Research–INRIA Joint Centre. 28, rue Jean Rostand, 91893 Orsay Cedex, France. email:[email protected]. Adaptation du Pas Deux-Point dans CMA-ES Résumé : Pas de résumé Mots-clés : Pas de motclef CMA-ES with Two-Point Step-Size Adaptation 3