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Enhancing Evolutionary Optimization Performance Under Byzantine Fault Conditions

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Hybrid Artificial Intelligent Systems (HAIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14001))

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Abstract

We evaluate the performance of panmictic evolutionary algorithms (EAs) in Byzantine environments, where fitness values are unreliable due to the potential presence of malicious agents. We investigate the impact of this phenomenon on the performance of the algorithm considering two different models of malicious behavior of different severity, taking the unreliability rate of the environment as a control parameter. We observe how there can be a significant toll in the quality of the results as the prevalence of cheating behavior increases, even for simple functions. Subsequently, we endow the EA with mechanisms based on redundant computation to cope with this issue, and examine their effectiveness. Our findings indicate that while a mechanism based on statistical averaging can be an effective approach under a relatively benign fault model, more hostile environments are better tackled via an approach based on majority voting.

This work is supported by Spanish Ministry of Science and Innovation under project Bio4Res (PID2021-125184NB-I00 – http://bio4res.lcc.uma.es) and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech.

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Acknowledgments

The author thanks Daan van den Berg (VU Amsterdam) for interesting discussions arising from a previous work [3].

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Authors and Affiliations

  1. Department of Lenguajes y Ciencias de la Computación, ETSI Informática, Campus de Teatinos, Universidad de Málaga, 29071, Málaga, Spain

    Carlos Cotta

  2. ITIS Software, Universidad de Málaga, Málaga, Spain

    Carlos Cotta

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  1. Carlos Cotta
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Correspondence to Carlos Cotta .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of Leon, León, Spain

    Hilde Pérez García

  3. University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  4. Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. University of A Coruña, Ferrol - Coruña, Spain

    José Luis Calvo Rolle

  8. University of A Coruña, Ferrol - Coruña, Spain

    Héctor Quintián

  9. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Cotta, C. (2023). Enhancing Evolutionary Optimization Performance Under Byzantine Fault Conditions. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_29

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  • DOI: https://doi.org/10.1007/978-3-031-40725-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40724-6

  • Online ISBN: 978-3-031-40725-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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