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European Conference on Genetic Programming (Part of EvoStar)

EuroGP 2021: Genetic Programming pp 198–213Cite as

Probabilistic Grammatical Evolution

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12691))

Abstract

Grammatical Evolution (GE) is one of the most popular Genetic Programming (GP) variants, and it has been used with success in several problem domains. Since the original proposal, many enhancements have been proposed to GE in order to address some of its main issues and improve its performance.

In this paper we propose Probabilistic Grammatical Evolution (PGE), which introduces a new genotypic representation and new mapping mechanism for GE. Specifically, we resort to a Probabilistic Context-Free Grammar (PCFG) where its probabilities are adapted during the evolutionary process, taking into account the productions chosen to construct the fittest individual. The genotype is a list of real values, where each value represents the likelihood of selecting a derivation rule. We evaluate the performance of PGE in two regression problems and compare it with GE and Structured Grammatical Evolution (SGE).

The results show that PGE has a better performance than GE, with statistically significant differences, and achieved similar performance when comparing with SGE.

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Acknowledgements

This work is partially funded by the project grant DSAIPA/DS/0022/2018 (GADgET), by national funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the project CISUC - UID/CEC/00326/2020 and by European Social Fund, through the Regional Operational Program Centro 2020. We also thank the NVIDIA Corporation for the hardware granted to this research.

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Authors and Affiliations

  1. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II - Pinhal de Marrocos, 3030, Coimbra, Portugal

    Jessica Mégane, Nuno Lourenço & Penousal Machado

Authors
  1. Jessica Mégane
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  2. Nuno Lourenço
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  3. Penousal Machado
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Correspondence to Jessica Mégane .

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Editors and Affiliations

  1. Queen’s University, Kingston, ON, Canada

    Ting Hu

  2. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  3. University of Trieste, Trieste, Italy

    Eric Medvet

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Mégane, J., Lourenço, N., Machado, P. (2021). Probabilistic Grammatical Evolution. In: Hu, T., Lourenço, N., Medvet, E. (eds) Genetic Programming. EuroGP 2021. Lecture Notes in Computer Science(), vol 12691. Springer, Cham. https://doi.org/10.1007/978-3-030-72812-0_13

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  • DOI: https://doi.org/10.1007/978-3-030-72812-0_13

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  • Online ISBN: 978-3-030-72812-0

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