QR mutations improve many evolution strategies -a lot on highly multimodal problems

Fabien Teytaud 1 Olivier Teytaud 2
2 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Previous studies have shown the efficiency of using quasi-random mutations on the well-know CMA evolution strategy [13]. Quasi-random mutations have many advantages, in particular their application is stable, efficient and easy to use. In this article, we extend this principle by applying quasi-random mutations on several well known continuous evolutionary algorithms (SA, CMSA, CMA) and do it on several old and new test functions, and with several criteria. The results point out a clear improvement compared to the baseline, in all cases, and in particular for moderate computational budget.
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Communication dans un congrès
T. Friedrich and F. Neumann. ACM-GECCO'16, Jul 2016, Denver, United States. pp.35-36, Poster in GECCO'16 Companion. 〈10.1145/1235〉
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Fabien Teytaud, Olivier Teytaud. QR mutations improve many evolution strategies -a lot on highly multimodal problems. T. Friedrich and F. Neumann. ACM-GECCO'16, Jul 2016, Denver, United States. pp.35-36, Poster in GECCO'16 Companion. 〈10.1145/1235〉. 〈hal-01406727〉

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