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Parameter Tuning for the \({(1 + (\lambda , \lambda ))}\) Genetic Algorithm Using Landscape Analysis and Machine Learning

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Applications of Evolutionary Computation (EvoApplications 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13224))

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Abstract

The choice of parameter values in evolutionary algorithms greatly affects their performance. Many popular parameter tuning techniques are limited by the tuning budget for finding a good set of parameter values. Recently, we proposed an approach to parameter tuning that uses fitness landscape analysis and machine learning to recommend good parameter values for problem instances based on their landscape features. Using fitness landscape features allows to identify similar problems and use parameter tuning data obtained on benchmark problems, significantly reducing the tuning budget requirements.

In this paper, we present our study of the landscape-aware parameter tuning approach for the \({(1 + (\lambda , \lambda ))}\) genetic algorithm. We evaluate the performance of the algorithm tuned by this approach on the linear integer weights problem and the MAX-3SAT problem, in addition to the W-model problem used for the collection of training data. Our results suggest that the proposed approach allows to make meaningful parameter choices and shows good performance without high fitness evaluation budget requirements.

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Notes

  1. 1.

    https://github.com/vmironovich/generic-onell.

  2. 2.

    https://github.com/engich/1ll-param-model.

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Acknowledgments

The reported study was funded by RFBR and CNRS, project number 20-51-15009.

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Authors and Affiliations

  1. ITMO University, 49 Kronverkskiy pr., Saint Petersburg, 197101, Russia

    Maxim Pikalov & Vladimir Mironovich

Authors
  1. Maxim Pikalov
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  2. Vladimir Mironovich
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Corresponding author

Correspondence to Vladimir Mironovich .

Editor information

Editors and Affiliations

  1. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

  2. Universidad Complutense de Madrid, Madrid, Spain

    J. Ignacio Hidalgo

  3. Edinburgh Napier University, Edinburgh, UK

    Kehinde Oluwatoyin Babaagba

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Pikalov, M., Mironovich, V. (2022). Parameter Tuning for the \({(1 + (\lambda , \lambda ))}\) Genetic Algorithm Using Landscape Analysis and Machine Learning. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_44

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  • DOI: https://doi.org/10.1007/978-3-031-02462-7_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02461-0

  • Online ISBN: 978-3-031-02462-7

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