Evolution Strategies

Nikolaus Hansen 1, 2 Dirk Arnold 3 Anne Auger 2
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Evolution strategies are evolutionary algorithms that date back to the 1960s and that are most commonly applied to black-box optimization problems in continuous search spaces. Inspired by biological evolution, their original formulation is based on the application of mutation, recombination and selection in populations of candidate solutions. From the algorithmic viewpoint, evolution strategies are optimization methods that sample new candidate solutions stochastically, most commonly from a multivariate normal probability distribution. Their two most prominent design principles are unbiasedness and adaptive control of parameters of the sample distribution. In this overview the important concepts of success based step-size control, self-adaptation and derandomization are covered, as well as more recent developments like covariance matrix adaptation and natural evolution strategies. The latter give new insights into the fundamental mathematical rationale behind evolution strategies. A broad discussion of theoretical results includes progress rate results on various function classes and convergence proofs for evolution strategies.
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Nikolaus Hansen, Dirk Arnold, Anne Auger. Evolution Strategies. Janusz Kacprzyk; Witold Pedrycz. Handbook of Computational Intelligence, Springer, 2015, 978-3-622-43504-5. ⟨hal-01155533⟩

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