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- Tutorial CMA-ES: evolution strategies and covariance matrix adaptation
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Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed
GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computationIn this paper, we study the performance of IPOP-saACM-ES and BIPOP-saACM-ES, recently proposed self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategies. Both algorithms were tested using restarts till a total number of function ...
Bi-population CMA-ES agorithms with surrogate models and line searches
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computationIn 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 ...
CMA-ES: evolution strategies and covariance matrix adaptation
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationEvolution Strategies (ESs) and many continuous domain Estimation of Distribution Algorithms (EDAs) are stochastic optimization procedures that sample a multivariate normal (Gaussian) distribution in the continuous search space, Rn. Many of them can be ...
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