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A self-adaptive Multimeme Memetic Algorithm co-evolving utility scores to control genetic operators and their parameter settings

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dc.creator Drake, John H.
dc.creator Altintas, Cevriye
dc.creator Asta, Shahriar
dc.creator Ozcan, Ender
dc.date 2016-11-30T21:00:00Z
dc.date.accessioned 2020-10-06T10:30:59Z
dc.date.available 2020-10-06T10:30:59Z
dc.identifier 6951de28-13f3-4221-b1fb-528d11f2f06c
dc.identifier 10.1016/j.asoc.2016.07.032
dc.identifier https://avesis.sdu.edu.tr/publication/details/6951de28-13f3-4221-b1fb-528d11f2f06c/oai
dc.identifier.uri http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/62441
dc.description 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 parameterised 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. (C) 2016 Elsevier B.V. All rights reserved.
dc.language eng
dc.rights info:eu-repo/semantics/closedAccess
dc.title A self-adaptive Multimeme Memetic Algorithm co-evolving utility scores to control genetic operators and their parameter settings
dc.type info:eu-repo/semantics/article


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