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Continuous Cartesian Genetic Programming with Particle Swarm Optimization

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Book cover Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 941))

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Abstract

Cartesian Genetic Programming (CGP) is a type of Genetic Programming, which uses a sequence of integers to represent an executable graph structure. The most common way of optimizing the CGP is to use a simple evolutionary strategy with mutations, which randomly changes the integer values of integer sequence. We propose an alternative genotype-phenotype mapping procedure for CGP allowing usage of real-valued numbers in genotype. Novel representation allows continuous transition between various functions and inputs of each given node (hence the name, Continuous CGP), which means, that the optimization of CGP individual is transformed from combinatorial optimization problem to continuous optimization problem. This allows leveraging various metaheuristic optimization algorithms. In this paper, we present results obtained by Particle Swarm Optimization algorithm, showing that continuous representation is able to outperform classic CGP in some benchmarks and provides competitive results with one of the best performing symbolic regression systems in literature.

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Notes

  1. 1.

    https://github.com/Jarino/tengp.

  2. 2.

    https://github.com/Jarino/cgp-optimization.

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Acknowledgment

This work was partially supported by the Slovak Research and Development Agency under the contract APVV-16-0213 and by the Operational Programme Research & Innovation, funded by the ERDF, project No. ITMS 26240120039.

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Authors and Affiliations

  1. Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, IlkoviÄŤova 2, 842 16, Bratislava 4, Slovakia

    Jaroslav Loebl & Viera Rozinajová

Authors
  1. Jaroslav Loebl
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  2. Viera Rozinajová
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Corresponding author

Correspondence to Jaroslav Loebl .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Loebl, J., Rozinajová, V. (2020). Continuous Cartesian Genetic Programming with Particle Swarm Optimization. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_96

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