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Hybridizing Lévy Flights and Cartesian Genetic Programming for Learning Swarm-Based Optimization

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Advances in Computational Intelligence Systems (UKCI 2023)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1453))

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Abstract

Cartesian Genetic Programming is a well-established version of Genetic Programming and has meanwhile been applied to many use cases. The case of learning swarm behavior for optimization recently showed some fitness landscape characteristics that make program evolution harder due to the intrinsic barrier structure that is hard to pass by using standard mutation. In this paper, we explore possible improvements by replacing the standard uniform mutation by Lévy flights when training with a \((\mu +\lambda )\)-evolution strategy. We demonstrate the superiority of the new variation operation for training instances of the optimization learning problem and compare success rates and minimal computational effort.

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Authors and Affiliations

  1. University of Oldenburg, 26129, Oldenburg, Germany

    Jörg Bremer & Sebastian Lehnhoff

Authors
  1. Jörg Bremer
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  2. Sebastian Lehnhoff
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Correspondence to Jörg Bremer .

Editor information

Editors and Affiliations

  1. Aston University, Birmingham, UK

    Nitin Naik

  2. Cardiff Metropolitan University, Cardiff, UK

    Paul Jenkins

  3. Aston University, Birmingham, UK

    Paul Grace

  4. Northumbria University, Newcastle upon Tyne, UK

    Longzhi Yang

  5. Devi Ahilya University, Indore, India

    Shaligram Prajapat

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Bremer, J., Lehnhoff, S. (2024). Hybridizing Lévy Flights and Cartesian Genetic Programming for Learning Swarm-Based Optimization. In: Naik, N., Jenkins, P., Grace, P., Yang, L., Prajapat, S. (eds) Advances in Computational Intelligence Systems. UKCI 2023. Advances in Intelligent Systems and Computing, vol 1453. Springer, Cham. https://doi.org/10.1007/978-3-031-47508-5_24

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