Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Comparison-based optimizers need comparison-based surrogates
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Local meta-models for optimization using evolution strategies
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Ordinal regression in evolutionary computation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Investigating the local-meta-model CMA-ES for large population sizes
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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In this paper, we study the performance of IPOP-saACM-ES, recently proposed self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategy. The algorithm was 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. The experiments show that the surrogate model control allows IPOP-saACM-ES to be as robust as the original IPOP-aCMA-ES and outperforms the latter by a factor from 2 to 3 on 6 benchmark problems with moderate noise. On 15 out of 30 benchmark problems in dimension 20, IPOP-saACM-ES exceeds the records observed during BBOB-2009 and BBOB-2010.