Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy

Ilya Loshchilov 1 Marc Schoenauer 1, 2 Michèle Sebag 1, 3
1 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 : This paper presents a novel mechanism to adapt surrogate-assisted population-based algorithms. This mechanism is applied to ACM-ES, a recently proposed surrogate-assisted variant of CMA-ES. The resulting algorithm, saACM-ES, adjusts online the lifelength of the current surrogate model (the number of CMA-ES generations before learning a new surrogate) and the surrogate hyper-parameters. Both heuristics significantly improve the quality of the surrogate model, yielding a significant speed-up of saACM-ES compared to the ACM-ES and CMA-ES baselines. The empirical validation of saACM-ES on the BBOB-2012 noiseless testbed demonstrates the efficiency and the scalability w.r.t the problem dimension and the population size of the proposed approach, that reaches new best results on some of the benchmark problems.
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Communication dans un congrès
Terence Soule and Jason H. Moore. Genetic and Evolutionary Computation Conference (GECCO 2012), Jul 2012, Philadelphia, United States. ACM Press, pp.321-328, 2012
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  • HAL Id : hal-00686570, version 1
  • ARXIV : 1204.2356

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Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy. Terence Soule and Jason H. Moore. Genetic and Evolutionary Computation Conference (GECCO 2012), Jul 2012, Philadelphia, United States. ACM Press, pp.321-328, 2012. 〈hal-00686570〉

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