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Hyper-parameter tuning for the (1+ (λ, λ)) GA

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Author(s)

Nguyen Dang, Carola Doerr

School/Research organisations

Abstract

It 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.
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Details

Original languageEnglish
Title of host publicationProceedings of the Genetic and Evolutionary Computation Conference (GECCO '19)
Place of PublicationNew York
PublisherACM
Pages889-897
Number of pages8
ISBN (Electronic)9781450361118
DOIs
Publication statusPublished - 13 Jul 2019
EventThe Genetic and Evolutionary Computation Conference (GECCO 2019 @ Prague) - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019
https://gecco-2019.sigevo.org/index.html

Conference

ConferenceThe Genetic and Evolutionary Computation Conference (GECCO 2019 @ Prague)
Abbreviated titleGECCO
CountryCzech Republic
CityPrague
Period13/07/1917/07/19
Internet address

    Research areas

  • Hyper-parameter tuning, Genetic algorithm, Evolutionary algorithm, Algorithm configuration

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ID: 258271522

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