Evolution Under Strong Noise: A Self-Adaptive Evolution Strategy Can Reach the Lower Performance Bound - The pcCMSA-ES

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9921)


According to a theorem by Astete-Morales, Cauwet, and Teytaud, “simple Evolution Strategies (ES)” that optimize quadratic functions disturbed by additive Gaussian noise of constant variance can only reach a simple regret log-log convergence slope \(\ge -1/2\) (lower bound). In this paper a population size controlled ES is presented that is able to perform better than the \(-1/2\) limit. It is shown experimentally that the pcCMSA-ES is able to reach a slope of \(-1\) being the theoretical lower bound of all comparison-based direct search algorithms.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Vorarlberg University of Applied Sciences, Research Center PPEDornbirnAustria

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