A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Log-Linear Convergence and Divergence of the Scale-Invariant (1+1)-ES in Noisy Environments
Algorithmica - Special Issue: Theory of Evolutionary Computation
Local meta-models for optimization using evolution strategies
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Optimal weighted recombination
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Comparison-based optimizers need comparison-based surrogates
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Paired-bacteria optimiser - A simple and fast algorithm
Information Processing Letters
Local-meta-model CMA-ES for partially separable functions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Splitting method for spatio-temporal sensors deployment in underwater systems
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
A new fitness estimation strategy for particle swarm optimization
Information Sciences: an International Journal
Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es)
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Bi-population CMA-ES agorithms with surrogate models and line searches
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Benchmarking the local metamodel CMA-ES on the noiseless BBOB'2013 test bed
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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For many real-life engineering optimization problems, the cost of one objective function evaluation can take several minutes or hours. In this context, a popular approach to reduce the number of function evaluations consists in building a (meta-)model of the function to be optimized using the points explored during the optimization process and replacing some (true) function evaluations by the function values given by the meta-model. In this paper, the local-meta-model CMA-ES (lmm-CMA) proposed by Kern et al. in 2006 coupling local quadratic meta-models with the Covariance Matrix Adaptation Evolution Strategy is investigated. The scaling of the algorithm with respect to the population size is analyzed and limitations of the approach for population sizes larger than the default one are shown. A new variant for deciding when the meta-model is accepted is proposed. The choice of the recombination type is also investigated to conclude that the weighted recombination is the most appropriate. Finally, this paper illustrates the influence of the different initial parameters on the convergence of the algorithm for multimodal functions.