Self-adapting fitness evaluation times for on-line evolution of simulated robots

Abstract

This paper is concerned with \textit{on-line} evolutionary robotics, where robot controllers are being evolved during a robots' operative time. This approach offers the ability to cope with environmental changes without human intervention, but to be effective it needs an automatic parameter control mechanism to adjust the evolutionary algorithm (EA) appropriately. In particular, mutation step sizes ($\sigma$) and the time spent on fitness evaluation ($\tau$) have a strong influence on the performance of an EA. In this paper, we introduce and experimentally validate a novel method for self-adapting $\tau$ during runtime. The results show that this mechanism is viable: the EA using this self-adaptative control scheme consistently shows decent performance without a priori tuning or human intervention during a run.