A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings
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 op...
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/36067/ |
| _version_ | 1848795216151052288 |
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| author | Özcan, Ender Drake, John H. Altıntaş, Cevriye Asta, Shahriar |
| author_facet | Özcan, Ender Drake, John H. Altıntaş, Cevriye Asta, Shahriar |
| author_sort | Özcan, Ender |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| 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 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. |
| first_indexed | 2025-11-14T19:28:33Z |
| format | Article |
| id | nottingham-36067 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:28:33Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-360672020-05-04T18:19:06Z https://eprints.nottingham.ac.uk/36067/ 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 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. Elsevier 2016-12-01 Article PeerReviewed Özcan, Ender, Drake, John H., Altıntaş, Cevriye and Asta, Shahriar (2016) A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings. Applied Soft Computing, 49 . pp. 81-93. ISSN 1872-9681 Memetic Algorithms Multimeme Memetic Algorithms Reinforcement Learning Hyper-heuristics Combinatorial Optimisation http://www.sciencedirect.com/science/article/pii/S1568494616303672?np=y doi:10.1016/j.asoc.2016.07.032 doi:10.1016/j.asoc.2016.07.032 |
| spellingShingle | Memetic Algorithms Multimeme Memetic Algorithms Reinforcement Learning Hyper-heuristics Combinatorial Optimisation Özcan, Ender Drake, John H. Altıntaş, Cevriye Asta, Shahriar A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings |
| title | A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings |
| title_full | A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings |
| title_fullStr | A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings |
| title_full_unstemmed | A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings |
| title_short | A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings |
| title_sort | self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings |
| topic | Memetic Algorithms Multimeme Memetic Algorithms Reinforcement Learning Hyper-heuristics Combinatorial Optimisation |
| url | https://eprints.nottingham.ac.uk/36067/ https://eprints.nottingham.ac.uk/36067/ https://eprints.nottingham.ac.uk/36067/ |