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Cooperative Co-Evolutionary Genetic Programming for High Dimensional Problems

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Parallel Problem Solving from Nature – PPSN XVI (PPSN 2020)

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Abstract

We propose a framework for Cooperative Co-Evolutionary Genetic Programming (CCGP) that considers co-evolution at three different abstraction levels: genotype, feature and output level. A thorough empirical evaluation is carried out on a real-world high dimensional ML problem (image denoising). Results indicate that GP’s performance is enhanced only when cooperation happens at an output level (ensemble-alike). The proposed co-evolutionary ensemble approach is compared against a canonical GP implementation and a GP customized for image processing tasks. Preliminary results show that the proposed framework obtains superior average performance in comparison to the other GP models. Our most relevant finding is the empirical evidence showing that the proposed CCGP model is a promising alternative to specialized GP implementations that require knowledge of the problem’s domain.

The last author gratefully acknowledges support from CONACyT grant no. 2016-01-1920 (Investigación en Fronteras de la Ciencia 2016) and from a SEP-Cinvestav grant (application no. 4).

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Acknowledgements

This work was partially supported by CONACyT under project grant A1-S-26314, Integración de Visión y Lenguaje mediante Representaciones Multimodales Aprendidas para Clasificación y Recuperación de Imágenes y Videos.

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Authors and Affiliations

  1. Instituto Nacional de Astrofísica, Óptica y Electrónica, 72840, Tonantzintla, Mexico

    Lino Rodriguez-Coayahuitl, Alicia Morales-Reyes & Hugo Jair Escalante

  2. CINVESTAV-IPN, Departamento de Computación, Mexico City, Mexico

    Hugo Jair Escalante & Carlos A. Coello Coello

Authors
  1. Lino Rodriguez-Coayahuitl
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  2. Alicia Morales-Reyes
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  3. Hugo Jair Escalante
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  4. Carlos A. Coello Coello
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Corresponding author

Correspondence to Lino Rodriguez-Coayahuitl .

Editor information

Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Thomas Bäck

  2. Leiden University, Leiden, The Netherlands

    Mike Preuss

  3. Leiden University, Leiden, The Netherlands

    André Deutz

  4. Sorbonne University, Paris, France

    Hao Wang

  5. Sorbonne University, Paris, France

    Carola Doerr

  6. Leiden University, Leiden, The Netherlands

    Michael Emmerich

  7. University of Münster, Münster, Germany

    Heike Trautmann

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Rodriguez-Coayahuitl, L., Morales-Reyes, A., Escalante, H.J., Coello Coello, C.A. (2020). Cooperative Co-Evolutionary Genetic Programming for High Dimensional Problems. In: Bäck, T., et al. Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science(), vol 12270. Springer, Cham. https://doi.org/10.1007/978-3-030-58115-2_4

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  • DOI: https://doi.org/10.1007/978-3-030-58115-2_4

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