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A Meta-Genetic Algorithm for Hybridizing Metaheuristics

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Progress in Artificial Intelligence (EPIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10423))

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Abstract

The research presented in this paper forms part of the initiative aimed at automating the design of intelligent techniques to make them more accessible to non-experts. This study focuses on automating the hybridization of metaheuristics and parameter tuning of the individual metaheuristics. It is an initial attempt at testing the feasibility to automate this design process. A genetic algorithm is used for this purpose. Each hybrid metaheuristic is a combination of metaheuristics and corresponding parameter values. The genetic algorithm explores the space of these combinations. The genetic algorithm is evaluated by applying it to solve the symmetric travelling salesman problem. The evolved hybrid metaheuristics are found to perform competitively with the manually designed hybrid approaches from previous studies and outperform the metaheuristics applied individually. The study has also revealed the potential reusability of the evolved hybrids. Based on the success of this initial study, different problem domains shall be used to verify the automation approach to the design of hybrid metaheuristics.

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Notes

  1. 1.

    http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/.

  2. 2.

    Large-scale tsp solvers require special data structures and heuristics [17] which falls out of the scope of this study.

  3. 3.

    https://www.dropbox.com/s/2cyeywtxvd56rjr/parameter_settings.rtf?dl=0.

  4. 4.

    http://iridia.ulb.ac.be/irace/.

  5. 5.

    https://www.dropbox.com/s/01imrwvtc48ce18/offline_tuning.rtf?dl=0.

  6. 6.

    https://www.dropbox.com/s/0c7gtcijdlbx5w4/stat_analysis.rtf?dl=0.

  7. 7.

    https://www.dropbox.com/s/0c7gtcijdlbx5w4/stat_analysis.rtf?dl=0.

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Authors and Affiliations

  1. University of KwaZulu-Natal, Pietermaritzburg, 3209, South Africa

    Ahmed Hassan & Nelishia Pillay

Authors
  1. Ahmed Hassan
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  2. Nelishia Pillay
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Correspondence to Ahmed Hassan .

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Editors and Affiliations

  1. Universidade do Porto, Porto, Portugal

    Eugénio Oliveira

  2. Universidade do Porto, Porto, Portugal

    João Gama

  3. Polytechnic Institute of Porto, Porto, Portugal

    Zita Vale

  4. Universidade do Porto, Porto, Portugal

    Henrique Lopes Cardoso

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Hassan, A., Pillay, N. (2017). A Meta-Genetic Algorithm for Hybridizing Metaheuristics. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_31

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  • DOI: https://doi.org/10.1007/978-3-319-65340-2_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65339-6

  • Online ISBN: 978-3-319-65340-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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