A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming

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

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/887841
Keywords: Evolutionary Programming; Genetic Programming; Automatic Design; Hyper-heuristics; Continuous Optimization
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.asoc.2017.10.002
Depositing User: Ozcan, Dr Ender
Date Deposited: 17 Oct 2017 10:09
Last Modified: 04 May 2020 19:12
URI: https://eprints.nottingham.ac.uk/id/eprint/47294

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