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Automatically designing more general mutation operators of evolutionary programming for groups of function classes using a hyper-heuristic

Authors

Libin Hong

John H. Drake

John R. Woodward

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ENDER OZCAN ender.ozcan@nottingham.ac.uk
Professor of Computer Science and Operational Research



Abstract

In this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation operator for Evolutionary Programming. This is done using the Gaussian and uniform distributions as the terminal set, and arithmetic operators as the function set. The mutation operators are automatically designed for a specific function class. The contribution of this paper is to show that a GP can not only automatically design a mutation operator for Evolutionary Programming (EP) on functions generated from a specific function class, but also can design more general mutation operators on functions generated from groups of function classes. In addition, the automatically designed mutation operators also show good performance on new functions generated from a specific function class or a group of function classes.

Citation

Hong, L., Drake, J. H., Woodward, J. R., & Özcan, E. (2016). Automatically designing more general mutation operators of evolutionary programming for groups of function classes using a hyper-heuristic. In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference - GECCO '16. https://doi.org/10.1145/2908812.2908958

Conference Name The Genetic and Evolutionary Computation Conference (GECCO 2016)
End Date Jul 24, 2016
Acceptance Date Mar 20, 2016
Publication Date Jul 20, 2016
Deposit Date Aug 4, 2016
Publicly Available Date Aug 4, 2016
Peer Reviewed Peer Reviewed
Book Title Proceedings of the 2016 on Genetic and Evolutionary Computation Conference - GECCO '16
DOI https://doi.org/10.1145/2908812.2908958
Public URL https://nottingham-repository.worktribe.com/output/800167
Publisher URL http://dl.acm.org/citation.cfm?doid=2908812.2908958

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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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