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Bayesian Immigrant Diploid Genetic Algorithm for Dynamic Environments

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Artificial Evolution (EA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12052))

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Abstract

In dynamic environments, the main aim of an optimization algorithm is to track the changes and to adapt the search process. In this paper, we propose an approach called the Bayesian Immigrant Diploid Genetic Algorithm (BIDGA). BIDGA uses implicit memory in the form of diploid chromosomes, combined with the Bayesian Optimization Algorithm (BOA), which is a form of Estimation of Distribution Algorithms (EDAs). Through the use of BOA, BIDGA is able to take into account epistasis in the form of binary relationships between the variables. Experiments show that the proposed approach is efficient and also indicates that exploiting interactions between variables is important to adapt to the newly formed environments.

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Notes

  1. 1.

    http://martinpelikan.net/software.html.

  2. 2.

    Each template should contain \(\rho \,\times \,l=l/K\) ones.

  3. 3.

    The exact results given in Table 2 in [18].

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Author information

Authors and Affiliations

  1. Istanbul Technical University, Sariyer, Istanbul, Turkey

    Emrullah Gazioglu & A. Sima Etaner-Uyar

Authors
  1. Emrullah Gazioglu
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  2. A. Sima Etaner-Uyar
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Corresponding author

Correspondence to Emrullah Gazioglu .

Editor information

Editors and Affiliations

  1. IRIMAS Institute, ENSISA, Mulhouse, France

    Lhassane Idoumghar

  2. Inria Bordeaux Sud-Ouest, IMB, University of Bordeaux, Talence, France

    Pierrick Legrand

  3. Research Center in Computer Science, Signal and Automatic Control of Lille, University of Lille, Villeneuve d'Ascq, France

    Arnaud Liefooghe

  4. GMPA, INRA, Thiverval-Grignon, France

    Evelyne Lutton

  5. Laboratoire d'Informatique, University of Tours, Tours, France

    Nicolas Monmarché

  6. Inria Saclay, University of Paris-Sud, Orsay, France

    Marc Schoenauer

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Gazioglu, E., Etaner-Uyar, A.S. (2020). Bayesian Immigrant Diploid Genetic Algorithm for Dynamic Environments. In: Idoumghar, L., Legrand, P., Liefooghe, A., Lutton, E., Monmarché, N., Schoenauer, M. (eds) Artificial Evolution. EA 2019. Lecture Notes in Computer Science(), vol 12052. Springer, Cham. https://doi.org/10.1007/978-3-030-45715-0_10

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  • DOI: https://doi.org/10.1007/978-3-030-45715-0_10

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  • Print ISBN: 978-3-030-45714-3

  • Online ISBN: 978-3-030-45715-0

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