Benchmarking a Weighted Negative Covariance Matrix Update on the BBOB-2010 Noiseless Testbed

Nikolaus Hansen 1 Raymond Ros 1
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 : We implement a weighted negative update of the covariance matrix in the CMA-ES—weighted active CMA-ES or, in short, aCMA-ES. We benchmark the IPOP-aCMA-ES and compare the performance with the IPOP-CMA-ES on the BBOB-2010 noiseless testbed in dimensions between 2 and 40. On nine out of 12 essentially unimodal functions, the aCMA is faster than CMA, in particular in larger dimension. On at least three functions it also leads to a (slightly) better scaling with the dimension. In none of the 24 benchmark functions aCMA appears to be significantly worse in any dimension. On two and five functions, IPOP-CMA-ES and IPOP-aCMA-ES respectively exceed the record observed during BBOB-2009.
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Nikolaus Hansen, Raymond Ros. Benchmarking a Weighted Negative Covariance Matrix Update on the BBOB-2010 Noiseless Testbed. Genetic And Evolutionary Computation Conference, Jul 2010, Portland, United States. pp.1673-1680, ⟨10.1145/1830761.1830788⟩. ⟨hal-00545728v2⟩

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