A Self-adaptive Coevolutionary Algorithm
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3638529.3654132.pdf
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1.03 MB
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Author(s)
Hevia Fajardo, Mario
Hemberg, Erik
Toutouh, Jamal
O'Reilly, Una-May
Lehre, Per Kristian
Date Issued
July 14, 2024
Publisher
ACM|Genetic and Evolutionary Computation Conference
Citation
Hevia Fajardo, Mario, Hemberg, Erik, Toutouh, Jamal, O'Reilly, Una-May and Lehre, Per Kristian. 2024. "A Self-adaptive Coevolutionary Algorithm."
Version
Final published version
Abstract
Coevolutionary algorithms are helpful computational abstractions of adversarial behavior and they demonstrate multiple ways that populations of competing adversaries influence one another. We introduce the ability for each competitor's mutation rate to evolve through self-adaptation. Because dynamic environments are frequently addressed with self-adaptation, we set up dynamic problem environments to investigate the impact of this ability. For a simple bilinear problem, a sensitivity analysis of the adaptive method's parameters reveals that it is robust over a range of multiplicative rate factors, when the rate is changed up or down with equal probability. An empirical study determines that each population's mutation rates converge to values close to the error threshold. Mutation rate dynamics are complex when both populations adapt their rates. Large scale empirical self-adaptation results reveal that both reasonable solutions and rates can be found. This addresses the challenge of selecting ideal static mutation rates in coevolutionary algorithms. The algorithm's payoffs are also robust. They are rarely poor and frequently they are as high as the payoff of the static rate to which they converge. On rare runs, they are higher.
Description
GECCO ’24, July 14–18, 2024, Melbourne, VIC, Australia
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3638529.3654132
