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GELAB and Hybrid Optimization Using Grammatical Evolution

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12489))

Abstract

Grammatical Evolution (GE) is a well known technique for program synthesis and evolution. Much has been written in the past about its research and applications. This paper presents a novel approach to performing hybrid optimization using GE. GE is used for structural search in the program space while other meta-heuristic algorithms are used for numerical optimization of the searched programs. The hybridised GE system was implemented in GELAB, a Matlab toolbox for GE.

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Notes

  1. 1.

    Web: http://bds.ul.ie/libGE/.

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Acknowledgements

The authors thank the anonymous referees for their time, comments and helpful suggestions. This work was supported, in part, by Science Foundation Ireland grant 13/RC/2094 and co-funded under the European Regional Development Fund through the Southern & Eastern Regional Operational Programme to Lero - the Irish Software Research Centre (www.lero.ie).

Author information

Authors and Affiliations

  1. Bio-computing and Developmental Systems (BDS) Research Group, University of Limerick, Limerick, Ireland

    Muhammad Adil Raja, Aidan Murphy & Conor Ryan

  2. The Irish Software Research Centre, Lero, University of Limerick, Limerick, Ireland

    Muhammad Adil Raja, Aidan Murphy & Conor Ryan

  3. Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland

    Muhammad Adil Raja, Aidan Murphy & Conor Ryan

Authors
  1. Muhammad Adil Raja
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  2. Aidan Murphy
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  3. Conor Ryan
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Corresponding author

Correspondence to Muhammad Adil Raja .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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Raja, M.A., Murphy, A., Ryan, C. (2020). GELAB and Hybrid Optimization Using Grammatical Evolution. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_26

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  • DOI: https://doi.org/10.1007/978-3-030-62362-3_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62361-6

  • Online ISBN: 978-3-030-62362-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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