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International Conference on Swarm Intelligence

ANTS 2018: Swarm Intelligence pp 342–350Cite as

Does the ACO\(\mathbb {_R}\) Algorithm Benefit from the Use of Crossover?

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11172))

Abstract

The ACO\(\mathbb {_R}\) algorithm is based on the Ant Colony Optimization (ACO) metaphor, and a crossover operator does not naturally within this metaphor. In spite of this, we investigate in this paper whether the performance of ACO\(\mathbb {_R}\) would benefit from the deployment, with a fixed probability, of a crossover operator. Our extensive experimental evaluation uses two applications: (1) training feedforward neural networks for classification using 65 benchmark datasets from the UCI repository; and (2) optimizing several popular synthetic benchmark continuous-domain functions with the number of dimensions varying from 10 up to 10,000. Our experimental results confirm that the use of crossover does improve performance on both applications to a statistically significant extent.

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Acknowledgments

Partial support of a grant from the Brandon University Research Council (BURC) is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Brandon University, Brandon, Canada

    Ashraf M. Abdelbar

  2. School of Computing, University of Kent, Canterbury, UK

    Khalid M. Salama

Authors
  1. Ashraf M. Abdelbar
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  2. Khalid M. Salama
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Corresponding author

Correspondence to Ashraf M. Abdelbar .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles, Brussels, Belgium

    Mauro Birattari

  3. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  4. University of Southern Denmark, Odense, Denmark

    Anders L. Christensen

  5. University of Sheffield, Sheffield, UK

    Andreagiovanni Reina

  6. National Research Council, Rome, Italy

    Vito Trianni

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Abdelbar, A.M., Salama, K.M. (2018). Does the ACO\(\mathbb {_R}\) Algorithm Benefit from the Use of Crossover?. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A., Reina, A., Trianni, V. (eds) Swarm Intelligence. ANTS 2018. Lecture Notes in Computer Science(), vol 11172. Springer, Cham. https://doi.org/10.1007/978-3-030-00533-7_28

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  • DOI: https://doi.org/10.1007/978-3-030-00533-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00532-0

  • Online ISBN: 978-3-030-00533-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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