A Survey on Cooperative Co-evolutionary Algorithms

Ma, X, Li, X, Zhang, Q, Tang, K, Liang, Z, Xie, W and Zhu, Z 2019, 'A Survey on Cooperative Co-evolutionary Algorithms', IEEE Transactions on Evolutionary Computation, vol. 16, no. 1, pp. 37-46.


Document type: Journal Article
Collection: Journal Articles

Title A Survey on Cooperative Co-evolutionary Algorithms
Author(s) Ma, X
Li, X
Zhang, Q
Tang, K
Liang, Z
Xie, W
Zhu, Z
Year 2019
Journal name IEEE Transactions on Evolutionary Computation
Volume number 16
Issue number 1
Start page 37
End page 46
Total pages 10
Publisher IEEE
Abstract IEEE The first cooperative co-evolutionary algorithm (CCEA) was proposed by Potter and De Jong in 1994 and since then many CCEAs have been proposed and successfully applied to solving various complex optimization problems. In applying CCEAs, the complex optimization problem is decomposed into multiple subproblems, and each subproblem is solved with a separate subpopulation, evolved by an individual evolutionary algorithm (EA). Through cooperative co-evolution of multiple EA subpopulations, a complete problem solution is acquired by assembling the representative members from each subpopulation. The underlying divide-and-conquer and collaboration mechanisms enable CCEAs to tackle complex optimization problems efficiently, and hence CCEAs have been attracting wide attention in the EA community. This paper presents a comprehensive survey of these CCEAs, covering problem decomposition, collaborator selection, individual fitness evaluation, subproblem resource allocation, implementations, benchmark test problems, control parameters, theoretical analyses, and applications. The unsolved challenges and potential directions for their solutions are discussed.
Subject Neural, Evolutionary and Fuzzy Computation
Optimisation
Keyword(s) Benchmark testing
Computer science
Cooperative co-evolutionary algorithm
evolutionary algorithm
genetic algorithm.
Genetic algorithms
Google
Optimization
Perturbation methods
Resource management
DOI - identifier 10.1109/TEVC.2018.2868770
Copyright notice © 2018 IEEE
ISSN 1089-778X
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 11 Abstract Views  -  Detailed Statistics
Created: Mon, 29 Apr 2019, 13:04:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us