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A Gender Genetic Algorithm and Its Comparison with Conventional Genetic Algorithm

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Advances in Neural Computation, Machine Learning, and Cognitive Research VI (NEUROINFORMATICS 2022)

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Abstract

This study presents a new variety of gender genetic algorithm (GGA). Using the example of five test optimization problems, its superiority over conventional genetic algorithm (GA) with an elitism operator is shown. The analysis of the novel GGA on multi-extreme optimization functions confirms the effectiveness of the idea of gender separation of the population. Based on the results obtained from experimental data and on their analysis, a more advanced modification of GGA is proposed, using determination of the offspring's gender based on the combination of parental chromosomes in it.

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Author information

Authors and Affiliations

  1. Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow, Russia

    Gavriil Kupriyanov

  2. D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow, Russia

    Igor Isaev & Sergey Dolenko

  3. Kotelnikov Institute of Radio Engineering and Electronics, Russian Academy of Sciences, Moscow, Russia

    Igor Isaev

Authors
  1. Gavriil Kupriyanov
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  2. Igor Isaev
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  3. Sergey Dolenko
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Corresponding author

Correspondence to Gavriil Kupriyanov .

Editor information

Editors and Affiliations

  1. Scientific Research Institute for System Analysis, Russian Academy of Sciences, Moscow, Russia

    Boris Kryzhanovsky

  2. Scientific Research Institute for System Analysis, Russian Academy of Sciences, Moscow, Russia

    Witali Dunin-Barkowski

  3. Scientific Research Institute for System Analysis, Russian Academy of Sciences, Moscow, Russia

    Vladimir Redko

  4. Moscow Aviation Institute (National Research University), Moscow, Russia

    Yury Tiumentsev

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Kupriyanov, G., Isaev, I., Dolenko, S. (2023). A Gender Genetic Algorithm and Its Comparison with Conventional Genetic Algorithm. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research VI. NEUROINFORMATICS 2022. Studies in Computational Intelligence, vol 1064. Springer, Cham. https://doi.org/10.1007/978-3-031-19032-2_16

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