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BI-population CMA-ES Algorithms with Surrogate Models and Line Searches

Ilya Loshchilov 1 Marc Schoenauer 2, 3 Michèle Sebag 3
1 Laboratory of Intelligent Systems (LIS)
LIS - Laboratory of Intelligent Systems
2 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 : In this paper, three extensions of the BI-population Covariance Matrix Adaptation Evolution Strategy with weighted active covariance matrix update (BIPOP-aCMA-ES) are investigated. First, to address expensive optimization, we benchmark a recently proposed extension of the self-adaptive surrogate-assisted CMA-ES which benefits from more intensive surrogate model exploitation (BIPOP-saACM-k). Second, to address separable optimization, we propose a hybrid of BIPOP-aCMA-ES and STEP algorithm with coordinate-wise line search (BIPOP-aCMA-STEP). Third, we propose HCMA, a hybrid of BIPOP-saACM-k, STEP and NEWUOA to benefit both from surrogate models and line searches. All algorithms were tested on the noiseless BBOB testbed using restarts till a total number of function evaluations of $10^6n$ was reached, where $n$ is the dimension of the function search space. The comparison shows that BIPOP-saACM-k outperforms its predecessor BIPOP-saACM up to a factor of 2 on ill-conditioned problems, while BIPOP-aCMA-STEP outperforms the original BIPOP-based algorithms on separable functions. The hybrid HCMA algorithm demonstrates the best overall performance compared to the best algorithms of the BBOB-2009, BBOB-2010 and BBOB-2012 when running for more than $100n$ function evaluations.
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Contributor : Loshchilov Ilya <>
Submitted on : Saturday, May 4, 2013 - 11:45:36 AM
Last modification on : Wednesday, September 16, 2020 - 5:08:51 PM
Long-term archiving on: : Monday, August 5, 2013 - 4:03:03 AM


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  • HAL Id : hal-00818596, version 2



Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. BI-population CMA-ES Algorithms with Surrogate Models and Line Searches. Workshop Proceedings of the (GECCO) Genetic and Evolutionary Computation Conference, Jul 2013, Amsterdam, Netherlands. pp.8. ⟨hal-00818596v2⟩



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