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Article

Archive-based cooperative coevolutionary algorithms

Authors:

Joseph F. HarrisonAuthors Info & Claims

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

Pages 345 - 352

https://doi.org/10.1145/1143997.1144060

Published: 08 July 2006 Publication History

Abstract

Archive-based cooperative coevolutionary algorithms attempt to retain a set of individuals which act as good collaborators for other coevolved individuals in the evolutionary system. We introduce a new archive-based algorithm, called iCCEA, which compares favorably with other cooperative coevolutionary algorithms. We explain the current problems with cooperative coevolution which have given rise to archive methods, detail the iCCEA algorithm, compare it against other traditional and archive-based methods on basic problem domains, and discuss the reasons behind the performance of various algorithms.

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https://doi.org/10.1080/00207543.2025.2453651
Abdul Halim ADas SIsmail IAbdul Halim ADas SIsmail I(2025)Location, Size, and CapacityInto a Deeper Understanding of Evolutionary Computing: Exploration, Exploitation, and Parameter Control10.1007/978-3-031-75577-4_1(1-152)Online publication date: 18-Jan-2025
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Qiu WHu XSong AZhang JChen W(2024)A Scalable Parallel Coevolutionary Algorithm With Overlapping Cooperation for Large-Scale Network-Based Combinatorial OptimizationIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.338975154:8(4806-4818)Online publication date: Aug-2024
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Index Terms

Archive-based cooperative coevolutionary algorithms
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Multi-agent systems

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cover image ACM Conferences

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

July 2006

2004 pages

ISBN:1595931864

DOI:10.1145/1143997

General Chair:
Mike Cattolico
Tiger Mountain Scientific, Inc., USA

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 July 2006

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GECCO06

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GECCO06: Genetic and Evolutionary Computation Conference

July 8 - 12, 2006

Washington, Seattle, USA

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GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Zeiträg YFigueira J(2025)A novel machine-learning rolling horizon heuristic for dynamic lot-sizing and job shop scheduling problemsInternational Journal of Production Research10.1080/00207543.2025.2453651(1-27)Online publication date: 23-Jan-2025
https://doi.org/10.1080/00207543.2025.2453651
Abdul Halim ADas SIsmail IAbdul Halim ADas SIsmail I(2025)Location, Size, and CapacityInto a Deeper Understanding of Evolutionary Computing: Exploration, Exploitation, and Parameter Control10.1007/978-3-031-75577-4_1(1-152)Online publication date: 18-Jan-2025
https://doi.org/10.1007/978-3-031-75577-4_1
Qiu WHu XSong AZhang JChen W(2024)A Scalable Parallel Coevolutionary Algorithm With Overlapping Cooperation for Large-Scale Network-Based Combinatorial OptimizationIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.338975154:8(4806-4818)Online publication date: Aug-2024
https://doi.org/10.1109/TSMC.2024.3389751
Zeiträg YRui Figueira JFigueira G(2024)A cooperative coevolutionary hyper-heuristic approach to solve lot-sizing and job shop scheduling problems using genetic programmingInternational Journal of Production Research10.1080/00207543.2023.2301044(1-28)Online publication date: 11-Jan-2024
https://doi.org/10.1080/00207543.2023.2301044
Zaki TZeiträg YNeves RFigueira J(2024)A cooperative coevolutionary genetic programming hyper-heuristic for multi-objective makespan and cost optimization in cloud workflow schedulingComputers & Operations Research10.1016/j.cor.2024.106805172(106805)Online publication date: Dec-2024
https://doi.org/10.1016/j.cor.2024.106805
Sharifi SShin DBriand LAschbacher N(2023)Identifying the Hazard Boundary of ML-Enabled Autonomous Systems Using Cooperative Coevolutionary SearchIEEE Transactions on Software Engineering10.1109/TSE.2023.332757549:12(5120-5138)Online publication date: 1-Dec-2023
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Nadizar GMedvet E(2023)On the Effects of Collaborators Selection and Aggregation in Cooperative Coevolution: An Experimental AnalysisGenetic Programming10.1007/978-3-031-29573-7_19(292-307)Online publication date: 29-Mar-2023
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Akinola AWineberg MCoello Coello C(2020)Using implicit multi-objectives properties to mitigate against forgetfulness in coevolutionary algorithmsProceedings of the 2020 Genetic and Evolutionary Computation Conference10.1145/3377930.3389825(769-777)Online publication date: 26-Jun-2020
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N M Bazlur Rashid AChoudhury T(2019)Knowledge management overview of feature selection problem in high-dimensional financial data: cooperative co-evolution and MapReduce perspectivesProblems and Perspectives in Management10.21511/ppm.17(4).2019.2817:4(340-359)Online publication date: 26-Dec-2019
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Ma XLi XZhang QTang KLiang ZXie WZhu Z(2019)A Survey on Cooperative Co-Evolutionary AlgorithmsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2018.286877023:3(421-441)Online publication date: Jun-2019
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