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Black-box Optimization Benchmarking of NIPOP-aCMA-ES and NBIPOP-aCMA-ES on the BBOB-2012 Noiseless Testbed

Ilya Loshchilov 1 Marc Schoenauer 1, 2 Michèle Sebag 2 
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 NIPOP-aCMA-ES and NBIPOP-aCMA-ES, recently proposed alternative restart strategies for CMA-ES. Both algorithms were 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. We compared new strategies to CMA-ES with IPOP and BIPOP restart schemes, two algorithms with one of the best overall performance observed during the BBOB-2009 and BBOB-2010. We also present the first benchmarking of BIPOP-CMA-ES with the weighted active covariance matrix update (BIPOP-aCMA-ES). The comparison shows that NIPOP-aCMA-ES usually outperforms IPOP-aCMA-ES and has similar performance with BIPOP-aCMA-ES, using only the regime of increasing the population size. The second strategy, NBIPOP-aCMA-ES, outperforms BIPOP-aCMA-ES in dimension 40 on weakly structured multi-modal functions thanks to the adaptive allocation of computation budgets between the regimes of restarts.
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Submitted on : Monday, October 1, 2012 - 4:55:04 PM
Last modification on : Tuesday, October 25, 2022 - 4:17:58 PM
Long-term archiving on: : Wednesday, January 2, 2013 - 8:20:08 AM


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  • HAL Id : hal-00737409, version 1


Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. Black-box Optimization Benchmarking of NIPOP-aCMA-ES and NBIPOP-aCMA-ES on the BBOB-2012 Noiseless Testbed. Workshop Proceedings of the {GECCO} Genetic and Evolutionary Computation Conference, Jul 2012, Philadelphia, United States. ⟨hal-00737409⟩



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