Black-Box Optimization Benchmarking the IPOP-CMA-ES on the Noiseless Testbed

Raymond Ros 1
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 : We benchmark the Covariance Matrix Adaptation-Evolution Strategy (CMA-ES) algorithm with an Increasing POPulation size (IPOP) restart policy on the BBOB noiseless testbed. The IPOP-CMA-ES is compared to the BIPOP-CMA-ES and is shown to perform at best two times faster on multi-modal functions f15 to f19 whereas it does not solve weakly structured functions f22, f23 and f24.
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Submitted on : Friday, April 16, 2010 - 2:05:47 PM
Last modification on : Monday, December 9, 2019 - 5:24:06 PM
Long-term archiving on: Tuesday, September 28, 2010 - 12:41:05 PM


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  • HAL Id : inria-00473777, version 1



Raymond Ros. Black-Box Optimization Benchmarking the IPOP-CMA-ES on the Noiseless Testbed. Genetic and Evolutionary Computation Conference 2010, Jul 2010, Portland, OR, United States. ⟨inria-00473777⟩



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