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

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

Authors

Eduardo Segredo

Eduardo Lalla-Ruiz

Stefan Vo�



Contributors

P Festa
Editor

M Sellmann
Editor

J Vanschoren
Editor

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.

Presentation Conference Type Conference Paper (Published)
Conference Name Learning and Intelligent OptimizatioN Conference LION 10
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
Contract Date May 31, 2016

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