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Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed

Ilya Loshchilov 1 Marc Schoenauer 1, 2 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
Abstract : In this paper, we study the performance of IPOP-saACM-ES, recently proposed self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategy. The algorithm was tested using restarts till a total number of function evaluations of 10^6D was reached, where D is the dimension of the function search space. The experiments show that the surrogate model control allows IPOP-saACM-ES to be as robust as the original IPOP-aCMA-ES and outperforms the latter by a factor from 2 to 3 on 6 benchmark problems with moderate noise. On 15 out of 30 benchmark problems in dimension 20, IPOP-saACM-ES exceeds the records observed during BBOB-2009 and BBOB-2010.
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Submitted on : Monday, April 23, 2012 - 6:05:49 PM
Last modification on : Friday, November 18, 2022 - 9:24:15 AM
Long-term archiving on: : Tuesday, July 24, 2012 - 2:36:02 AM


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  • HAL Id : hal-00690543, version 1
  • ARXIV : 1206.0974


Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed. Workshop Proceedings of the (GECCO) Genetic and Evolutionary Computation Conference, Jul 2012, Philadelphia, United States. ⟨hal-00690543⟩



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