Research Output

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

  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.

  • Date:

    01 December 2016

  • Publication Status:

    Published

  • Publisher

    Springer

  • DOI:

    10.1007/978-3-319-50349-3_25

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004.2 Systems analysis, design & performance

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 Festa, P., Sellmann, M. & Vanschoren, J. (Eds.). Learning and Intelligent Optimization: 10th International Conference, LION 10, Ischia, Italy, May 29 -- June 1, 2016, 296-305. doi:10.1007/978-3-319-50349-3_25. ISBN 978-3-319-50348-6, 978-3-319-50349-3,

Authors

Keywords

Global search, differential evolution, genetic algorithm, global continuous optimisation, Hyper-heuristic

Monthly Views:

Available Documents