Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms

Hugo I Alcaraz Herrera; John Cartlidge

Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

We propose substitution of the fittest (SF), a novel technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. The approach presented is domain-independent and requires no calibration. In a minimal domain, we perform a controlled evaluation of the ability to maintain engagement and the capacity to discover optimal solutions. Results demonstrate that the solution discovery performance of SF is comparable with other techniques in the literature, while SF also offers benefits including a greater ability to maintain engagement and a much simpler mechanism.

Original language	English
Title of host publication	13th International Conference on Evolutionary Computation Theory and Applications (ECTA)
Publication status	Accepted/In press - 31 Jul 2021
Event	13th International Conference on Evolutionary Computation Theory and Applications (ECTA) - Virtual (Online Streaming) Duration: 25 Oct 2021 → 27 Oct 2021 Conference number: 13

Conference

Conference	13th International Conference on Evolutionary Computation Theory and Applications (ECTA)
Abbreviated title	ECTA
Period	25/10/21 → 27/10/21

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https://arxiv.org/abs/2108.03156Licence: Other

Cite this

@inproceedings{71358a7427fa4f6485dc7781778d522c,

title = "Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms",

abstract = "We propose substitution of the fittest (SF), a novel technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. The approach presented is domain-independent and requires no calibration. In a minimal domain, we perform a controlled evaluation of the ability to maintain engagement and the capacity to discover optimal solutions. Results demonstrate that the solution discovery performance of SF is comparable with other techniques in the literature, while SF also offers benefits including a greater ability to maintain engagement and a much simpler mechanism.",

author = "{Alcaraz Herrera}, {Hugo I} and John Cartlidge",

year = "2021",

month = jul,

day = "31",

language = "English",

booktitle = "13th International Conference on Evolutionary Computation Theory and Applications (ECTA)",

note = "13th International Conference on Evolutionary Computation Theory and Applications (ECTA), ECTA ; Conference date: 25-10-2021 Through 27-10-2021",

}

Alcaraz Herrera, HI & Cartlidge, J 2021, Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms. in 13th International Conference on Evolutionary Computation Theory and Applications (ECTA). 13th International Conference on Evolutionary Computation Theory and Applications (ECTA), 25/10/21. <https://arxiv.org/abs/2108.03156>

TY - GEN

T1 - Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms

AU - Alcaraz Herrera, Hugo I

AU - Cartlidge, John

N1 - Conference code: 13

PY - 2021/7/31

Y1 - 2021/7/31

N2 - We propose substitution of the fittest (SF), a novel technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. The approach presented is domain-independent and requires no calibration. In a minimal domain, we perform a controlled evaluation of the ability to maintain engagement and the capacity to discover optimal solutions. Results demonstrate that the solution discovery performance of SF is comparable with other techniques in the literature, while SF also offers benefits including a greater ability to maintain engagement and a much simpler mechanism.

AB - We propose substitution of the fittest (SF), a novel technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. The approach presented is domain-independent and requires no calibration. In a minimal domain, we perform a controlled evaluation of the ability to maintain engagement and the capacity to discover optimal solutions. Results demonstrate that the solution discovery performance of SF is comparable with other techniques in the literature, while SF also offers benefits including a greater ability to maintain engagement and a much simpler mechanism.

M3 - Conference Contribution (Conference Proceeding)

BT - 13th International Conference on Evolutionary Computation Theory and Applications (ECTA)

T2 - 13th International Conference on Evolutionary Computation Theory and Applications (ECTA)

Y2 - 25 October 2021 through 27 October 2021

ER -

Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms

Abstract

Conference

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