Median-selection for parallel steady-state evolution strategies

Abstract

We describe a new selection scheme for steady-state evolution strategies, median selection. In steady-state algorithms, only one individual is generated and evaluated at each step and is immediately integrated into the population. This is especially well suited for parallel fitness evaluation in a multiprocessor environment. Previous steady-state selection schemes resembled (μ + λ) selection, which has a disadvantage in self-adaptation of the mutation step length. Median selection is similar to (μ + λ) selection. Median selection is compared with other steady-state selection schemes and with (μ + λ) selection on a uniprocessor and on a multiprocessor. It achieves equally good or better results as the best other selection scheme for a number of benchmark functions.