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A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings

Özcan, Ender; Drake, John H.; Altıntaş, Cevriye; Asta, Shahriar

Authors

John H. Drake

Cevriye Altıntaş

Shahriar Asta



Abstract

Memetic algorithms are a class of well-studied metaheuristics which combine evolutionary algorithms and local search techniques. A meme represents contagious piece of information in an adaptive information sharing system. The canonical memetic algorithm uses a fixed meme, denoting a hill climbing operator, to improve each solution in a population during the evolutionary search process. Given global parameters and multiple parametrised operators, adaptation often becomes a crucial constituent in the design of MAs. In this study, a self-adaptive self-configuring steady-state multimeme memetic algorithm (SSMMA) variant is proposed. Along with the individuals (solutions), SSMMA co-evolves memes, encoding the utility score for each algorithmic component choice and relevant parameter setting option. An individual uses tournament selection to decide which operator and parameter setting to employ at a given step. The performance of the proposed algorithm is evaluated on six combinatorial optimisation problems from a cross-domain heuristic search benchmark. The results indicate the success of SSMMA when compared to the static Mas as well as widely used self-adaptive Multimeme Memetic Algorithm from the scientific literature.

Citation

Özcan, E., Drake, J. H., Altıntaş, C., & Asta, S. (2016). A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings. Applied Soft Computing, 49, https://doi.org/10.1016/j.asoc.2016.07.032

Journal Article Type Article
Acceptance Date Jul 20, 2016
Online Publication Date Aug 16, 2016
Publication Date Dec 1, 2016
Deposit Date Aug 30, 2016
Publicly Available Date Aug 30, 2016
Journal Applied Soft Computing
Print ISSN 1568-4946
Electronic ISSN 1872-9681
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 49
DOI https://doi.org/10.1016/j.asoc.2016.07.032
Keywords Memetic Algorithms, Multimeme Memetic Algorithms,
Reinforcement Learning, Hyper-heuristics, Combinatorial Optimisation
Public URL http://eprints.nottingham.ac.uk/id/eprint/36067
Publisher URL http://www.sciencedirect.com/science/article/pii/S1568494616303672?np=y
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





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