Learning Probability Distributions in Continuous Evolutionary Algorithms - a Comparative Review

Kern, Stefan; Müller, Sibylle D.; Hansen, Nikolaus; Büche, Dirk; Očenášek, Jiří; Koumoutsakos, Petros

doi:10.1023/b:naco.0000036904.41423.1c

Cited by 69 publications

(83 citation statements)

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“…Thus, it is necessary to combine both techniques. For example, in context of other applications, we encounter some soft computing methods used for solution of the multi-modal problem [5,6]. Some of them utilize a hybrid genetic algorithm coupled with other computational algorithms [2].…”

Section: Related Work On the Inversion Of Resistivity Ac Measurementsmentioning

confidence: 99%

A hybrid method for inversion of 3D AC resistivity logging measurements

Smółka

Gajda-Zagórska

Schaefer

et al. 2015

Applied Soft Computing

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Section: Related Work On the Inversion Of Resistivity Ac Measurementsmentioning

confidence: 99%

A hybrid method for inversion of 3D AC resistivity logging measurements

Smółka

Gajda-Zagórska

Schaefer

et al. 2015

Applied Soft Computing

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“…To every individual a fitness value determined from the cost function is assigned. In our investigation we use the following simple evolutionary algorithm, for further details the reader is referred to [36][37][38][39]:…”

Section: Implicit Sgs-model Construction By Evolutionary Optimizationmentioning

confidence: 99%

Implicit subgrid-scale modeling by adaptive deconvolution

Adams

Hickel

Franz

2004

Journal of Computational Physics

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“…Rank-based weighted recombination has been employed by Hansen and Ostermeier [19] in connection with their covariance matrix adaptation evolution strategy (CMA-ES), and it has also been used in the comparative review of evolutionary algorithms by Kern et al [20]. In both references, it is suggested to assign positive weights of different magnitudes to the better 50% of the candidate solutions generated.…”

mentioning

confidence: 99%

Weighted multirecombination evolution strategies

Arnold¹

2006

Theoretical Computer Science

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Weighted recombination is a means for improving the local search performance of evolution strategies. It aims to make effective use of the information available, without significantly increasing computational costs per time step. In this paper, the potential speed-up resulting from using rank-based weighted multirecombination is investigated. Optimal weights are computed for the infinite-dimensional sphere model, and comparisons with the performance of strategies that do not make use of weighted recombination are presented. It is seen that unlike strategies that rely on unweighted recombination and truncation selection, weighted multirecombination evolution strategies are able to improve on the serial efficiency of the (1 + 1)-ES on the sphere. The implications of the use of weighted recombination for noisy optimization are studied, and parallels to the use of rescaled mutations are drawn. The significance of the findings is investigated in finite-dimensional search spaces.

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Learning Probability Distributions in Continuous Evolutionary Algorithms - a Comparative Review

Cited by 69 publications

References 0 publications

A hybrid method for inversion of 3D AC resistivity logging measurements

A hybrid method for inversion of 3D AC resistivity logging measurements

Implicit subgrid-scale modeling by adaptive deconvolution

Weighted multirecombination evolution strategies

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