On the Benefits of Populations for Noisy Optimization

Abstract

It is known that, in the absence of noise, no improvement in local performance can be gained from retaining candidate solutions other than the best one. Yet, it has been shown experimentally that, in the presence of noise, operating with a non-singular population of candidate solutions can have a marked and positive effect on the local performance of evolution strategies. So as to determine the reasons for the improved performance, we have studied the evolutionary dynamics of the (μ, λ)-ES in the presence of noise. Considering a simple, idealized environment, we have developed a moment-based approach that uses recent results involving concomitants of selected order statistics. This approach yields an intuitive explanation for the performance advantage of multi-parent strategies in the presence of noise. It is then shown that the idealized dynamic process considered does bear relevance to optimization problems in high-dimensional search spaces.