Hybridisation of Evolutionary Algorithms through hyper-heuristics for global continuous optimisation

Segredo, Eduardo; Lalla-Ruiz, Eduardo; Hart, Emma; Paechter, Ben; Vo�, Stefan

Abstract

Choosing the correct algorithm to solve a problem still remains an issue 40 years after the Algorithm Selection Problem was first posed. Here we propose a hyper-heuristic which can apply one of two meta-heuristics at the current stage of the search. A scoring function is used to select the most appropriate algorithm based on an estimate of the improvement that might be made by applying each algorithm. We use a differential evolution algorithm and a genetic algorithm as the two meta-heuristics and assess performance on a suite of 18 functions provided by the Generalization-based Contest in Global Optimization (genopt). The experimental evaluation shows that the hybridisation is able to provide an improvement with respect to the results obtained by both the differential evolution scheme and the genetic algorithm when they are executed independently. In addition, the high performance of our hybrid approach allowed two out of the three prizes available at genopt to be obtained.

Citation

Segredo, E., Lalla-Ruiz, E., Hart, E., Paechter, B., & Voß, S. (2016). Hybridisation of Evolutionary Algorithms through hyper-heuristics for global continuous optimisation. In P. Festa, M. Sellmann, & J. Vanschoren (Eds.), Learning and Intelligent Optimization: 10th International Conference, LION 10, Ischia, Italy, May 29 -- June 1, 2016 (296-305). https://doi.org/10.1007/978-3-319-50349-3_25

• Conference Name: Learning and Intelligent OptimizatioN Conference LION 10
• Conference Location: Ischia Island (Napoli), Italy
• Start Date: May 29, 2016
• End Date: Jun 1, 2016
• Acceptance Date: Apr 28, 2016
• Online Publication Date: Dec 1, 2016
• Publication Date: Dec 1, 2016
• Deposit Date: May 31, 2016
• Publicly Available Date: Dec 1, 2016
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 296-305
• Series Title: Lecture Notes in Computer Science: Theoretical Computer Science and General Issues
• Series Number: 10079
• Series ISSN: 0302-9743
• Book Title: Learning and Intelligent Optimization: 10th International Conference, LION 10, Ischia, Italy, May 29 -- June 1, 2016
• ISBN: 9783319503486; 9783319503493
• DOI: https://doi.org/10.1007/978-3-319-50349-3_25
• Keywords: Global search, differential evolution, genetic algorithm, global continuous optimisation, Hyper-heuristic
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/10329