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The Usability Argument for Refinement Typed Genetic Programming

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

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Abstract

The performance of Evolutionary Algorithms is frequently hindered by arbitrarily large search spaces. In order to overcome this challenge, domain-specific knowledge is often used to restrict the representation or evaluation of candidate solutions to the problem at hand. Due to the diversity of problems and the unpredictable performance impact, the encoding of domain-specific knowledge is a frequent problem in the implementation of evolutionary algorithms.

We propose the use of Refinement Typed Genetic Programming, an enhanced hybrid of Strongly Typed Genetic Programming (STGP) and Grammar-Guided Genetic Programming (GGGP) that features an advanced type system with polymorphism and dependent and refined types.

We argue that this approach is more usable for describing common problems in machine learning, optimisation and program synthesis, due to the familiarity of the language (when compared to GGGP) and the use of a unifying language to express the representation, the phenotype translation, the evaluation function and the context in which programs are executed.

This work was supported by LASIGE (UIDB/00408/2020) and the CMU|Portugal project CAMELOT (POCI-01-0247-FEDER-045915).

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Notes

  1. 1.

    We have developed a compiler from Æon to Java and an Æon interpreter in Python. In each case, the function would have to be defined in Java and Python.

  2. 2.

    Replacing the hole by a crash-inducing expression allows the program to compile or be interpreted. While this is out of scope, the reader may find more in [25].

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Authors and Affiliations

  1. LASIGE, Faculdade de Ciencias da Universidade de Lisboa, Lisbon, Portugal

    Alcides Fonseca, Paulo Santos & Sara Silva

Authors
  1. Alcides Fonseca
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  2. Paulo Santos
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  3. Sara Silva
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Correspondence to Alcides Fonseca .

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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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Fonseca, A., Santos, P., Silva, S. (2020). The Usability Argument for Refinement Typed Genetic Programming. 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_2

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  • DOI: https://doi.org/10.1007/978-3-030-58115-2_2

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