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

Hong, Libin; Drake, John H.; Woodward, John R.; �zcan, Ender

Abstract

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.

Citation

Hong, L., Drake, J. H., Woodward, J. R., & Özcan, E. (in press). A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming. Applied Soft Computing, 62, https://doi.org/10.1016/j.asoc.2017.10.002

• Journal Article Type: Article
• Acceptance Date: Oct 5, 2017
• Online Publication Date: Oct 14, 2017
• Deposit Date: Oct 17, 2017
• Publicly Available Date: Oct 15, 2018
• Journal: Applied Soft Computing
• Print ISSN: 1568-4946
• Electronic ISSN: 1872-9681
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 62
• DOI: https://doi.org/10.1016/j.asoc.2017.10.002
• Keywords: Evolutionary Programming; Genetic Programming; Automatic Design; Hyper-heuristics; Continuous Optimization
• Public URL: https://nottingham-repository.worktribe.com/output/887841
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S1568494617306051
• Contract Date: Oct 17, 2017