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Intensive Surrogate Model Exploitation in Self-adaptive Surrogate-assisted CMA-ES (saACM-ES)

Ilya Loshchilov 1, 2 Marc Schoenauer 1, 3 Michèle Sebag 1, 3 
1 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
2 Laboratory of Intelligent Systems (LIS)
LIS - Laboratory of Intelligent Systems
Abstract : This paper presents a new mechanism for a better exploitation of surrogate models in the framework of Evolution Strategies (ESs). This mechanism is instantiated here on the self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (saACM-ES), a recently proposed surrogate-assisted variant of CMA-ES. As well as in the original saACM-ES, the expensive function is optimized by exploiting the surrogate model, whose hyper-parameters are also optimized online. The main novelty concerns a more intensive exploitation of the surrogate model by using much larger population sizes for its optimization. The new variant of saACM-ES significantly improves the original saACM-ES and further increases the speed-up compared to the CMA-ES, especially on unimodal functions (e.g., on 20-dimensional Rotated Ellipsoid, saACM-ES is 6 times faster than aCMA-ES and almost by one order of magnitude faster than CMA-ES). The empirical validation on the BBOB-2013 noiseless testbed demonstrates the efficiency and the robustness of the proposed mechanism.
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Submitted on : Sunday, April 28, 2013 - 1:23:21 AM
Last modification on : Sunday, June 26, 2022 - 11:58:41 AM
Long-term archiving on: : Monday, July 29, 2013 - 2:35:13 AM


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  • HAL Id : hal-00818595, version 1



Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. Intensive Surrogate Model Exploitation in Self-adaptive Surrogate-assisted CMA-ES (saACM-ES). Genetic and Evolutionary Computation Conference (GECCO 2013), Jul 2013, Amsterdam, Netherlands. pp.439-446. ⟨hal-00818595⟩



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