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Coevolving memetic algorithms: A review and progress report

Smith, Jim

Authors

Jim Smith James.Smith@uwe.ac.uk
Professor in Interactive Artificial Intelligence



Abstract

Coevolving memetic algorithms are a family of metaheuristic search algorithms in which a rule-based representation of local search (LS) is coadapted alongside candidate solutions within a hybrid evolutionary system. Simple versions of these systems have been shown to outperform other nonadaptive memetic and evolutionary algorithms on a range of problems. This paper presents a rationale for such systems and places them in the context of other recent work on adaptive memetic algorithms. It then proposes a general structure within which a population of LS algorithms can be evolved in tandem with the solutions to which they are applied. Previous research started with a simple self-adaptive system before moving on to more complex models. Results showed that the algorithm was able to discover and exploit certain forms of structure and regularities within the problems. This "metalearning" of problem features provided a means of creating highly scalable algorithms. This work is briefly reviewed to highlight some of the important findings and behaviors exhibited. Based on this analysis, new results are then presented from systems with more flexible representations, which, again, show significant improvements. Finally, the current state of, and future directions for, research in this area is discussed. © 2007 IEEE.

Citation

Smith, J. E., & Smith, J. (2007). Coevolving memetic algorithms: A review and progress report. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 37(1), 6-17. https://doi.org/10.1109/TSMCB.2006.883273

Journal Article Type Review
Publication Date Feb 1, 2007
Journal IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Print ISSN 1083-4419
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Not Peer Reviewed
Volume 37
Issue 1
Pages 6-17
DOI https://doi.org/10.1109/TSMCB.2006.883273
Keywords adaptive systems, benchmark testing, encoding, evolutionary computation, genetic mutations, helium, system testing, evolutionary computation, learning, artificial intelligence, search problems,coevolving memetic algorithm, evolutionary algorithm, metaheur
Public URL https://uwe-repository.worktribe.com/output/1029803
Publisher URL http://dx.doi.org/10.1109/TSMCB.2006.883273

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