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dc.contributor.authorDang, Nguyen
dc.contributor.authorDoerr, Carola
dc.identifier.citationDang , N & Doerr , C 2019 , Hyper-parameter tuning for the (1+ (λ, λ)) GA . in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19) . ACM , New York , pp. 889-897 , The Genetic and Evolutionary Computation Conference (GECCO 2019 @ Prague) , Prague , Czech Republic , 13/07/19 .
dc.identifier.otherPURE: 258271522
dc.identifier.otherPURE UUID: 52cac5a5-dcec-45ad-9778-d40b553b5f97
dc.identifier.otherORCID: /0000-0002-2693-6953/work/59465048
dc.identifier.otherScopus: 85070633883
dc.identifier.otherWOS: 000523218400105
dc.description.abstractIt is known that the (1 + (λ, λ)) Genetic Algorithm (GA) with self-adjusting parameter choices achieves a linear expected optimization time on OneMax if its hyper-parameters are suitably chosen. However, it is not very well understood how the hyper-parameter settings influences the overall performance of the (1 + (λ, λ)) GA. Analyzing such multi-dimensional dependencies precisely is at the edge of what running time analysis can offer. To make a step forward on this question, we present an in-depth empirical study of the self-adjusting (1 + (λ, λ)) GA and its hyper-parameters. We show, among many other results, that a 15% reduction of the average running time is possible by a slightly different setup, which allows non-identical offspring population sizes of mutation and crossover phase, and more flexibility in the choice of mutation rate and crossover bias --- a generalization which may be of independent interest. We also show indication that the parametrization of mutation rate and crossover bias derived by theoretical means for the static variant of the (1 + (λ, λ)) GA extends to the non-static case.
dc.relation.ispartofProceedings of the Genetic and Evolutionary Computation Conference (GECCO '19)en
dc.rights©2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. This work has been made available online in accordance with the publisher's policies. This is the final published version of the work, which was originally published at
dc.subjectHyper-parameter tuningen
dc.subjectGenetic algorithmen
dc.subjectEvolutionary algorithmen
dc.subjectAlgorithm configurationen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectComputer Science (miscellaneous)en
dc.titleHyper-parameter tuning for the (1+ (λ, λ)) GAen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen

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