A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming
Evolutionary programming can solve black-box function optimisation problems by evolving a population of numerical vectors. The variation component in the evolutionary process is supplied by a mutation operator, which is typically a Gaussian, Cauchy, or Lévy probability distribution. In this paper, w...
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| Format: | Article |
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Elsevier
2017
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| Online Access: | https://eprints.nottingham.ac.uk/47294/ |
| _version_ | 1848797509871206400 |
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| author | Hong, Libin Drake, John H. Woodward, John R. Özcan, Ender |
| author_facet | Hong, Libin Drake, John H. Woodward, John R. Özcan, Ender |
| author_sort | Hong, Libin |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Evolutionary programming can solve black-box function optimisation problems by evolving a population of numerical vectors. The variation component in the evolutionary process is supplied by a mutation operator, which is typically a Gaussian, Cauchy, or Lévy probability distribution. In this paper, we use genetic programming to automatically generate mutation operators for an evolutionary programming system, testing the proposed approach over a set of function classes, which represent a source of functions. The empirical results over a set of benchmark function classes illustrate that genetic programming can evolve mutation operators which generalise well from the training set to the test set on each function class. The proposed method is able to outperform existing human designed mutation operators with statistical significance in most cases, with competitive results observed for the rest. |
| first_indexed | 2025-11-14T20:05:01Z |
| format | Article |
| id | nottingham-47294 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:05:01Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-472942020-05-04T19:12:42Z https://eprints.nottingham.ac.uk/47294/ A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming Hong, Libin Drake, John H. Woodward, John R. Özcan, Ender Evolutionary programming can solve black-box function optimisation problems by evolving a population of numerical vectors. The variation component in the evolutionary process is supplied by a mutation operator, which is typically a Gaussian, Cauchy, or Lévy probability distribution. In this paper, we use genetic programming to automatically generate mutation operators for an evolutionary programming system, testing the proposed approach over a set of function classes, which represent a source of functions. The empirical results over a set of benchmark function classes illustrate that genetic programming can evolve mutation operators which generalise well from the training set to the test set on each function class. The proposed method is able to outperform existing human designed mutation operators with statistical significance in most cases, with competitive results observed for the rest. Elsevier 2017-10-14 Article PeerReviewed Hong, Libin, Drake, John H., Woodward, John R. and Özcan, Ender (2017) A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming. Applied Soft Computing, 62 . pp. 162-175. ISSN 1872-9681 Evolutionary Programming; Genetic Programming; Automatic Design; Hyper-heuristics; Continuous Optimization http://www.sciencedirect.com/science/article/pii/S1568494617306051 doi:10.1016/j.asoc.2017.10.002 doi:10.1016/j.asoc.2017.10.002 |
| spellingShingle | Evolutionary Programming; Genetic Programming; Automatic Design; Hyper-heuristics; Continuous Optimization Hong, Libin Drake, John H. Woodward, John R. Özcan, Ender A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming |
| title | A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming |
| title_full | A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming |
| title_fullStr | A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming |
| title_full_unstemmed | A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming |
| title_short | A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming |
| title_sort | hyper-heuristic approach to automated generation of mutation operators for evolutionary programming |
| topic | Evolutionary Programming; Genetic Programming; Automatic Design; Hyper-heuristics; Continuous Optimization |
| url | https://eprints.nottingham.ac.uk/47294/ https://eprints.nottingham.ac.uk/47294/ https://eprints.nottingham.ac.uk/47294/ |