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Feature Encapsulation by Stages Using Grammatical Evolution

Authors:

Darian Reyes Fernández De Bulnes,

Allan De Lima,

Aidan Murphy,

Douglas Mota Dias,

Conor RyanAuthors Info & Claims

GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 531 - 534

https://doi.org/10.1145/3638530.3654097

Published: 01 August 2024 Publication History

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Abstract

This paper introduces a novel mechanism, Feature Encapsulation by Stages (FES), to encapsulate and transfer features as knowledge in a staged manner within the evolutionary process. Encapsulation happens via input space expansion in one or more stages by adding the best-of-run individual as an additional input. This input space expansion is managed by augmenting the grammar. We study the feasibility of dynamically modifying the grammar and reinitialising the population to make way for new individuals which quickly evolve to a better fitness level. Five different approaches to stage management are examined. In addition, three different selection processes, namely, Tournament, Lexicase and Lexi², are used to investigate which is best suited to use with our encapsulation procedure. We benchmark our procedure on two problem domains, Boolean and Classification, and demonstrate these staging strategies lead to significantly better results. Statistical tests show our FES outperforms the standard baseline in all Boolean problems, with a 4-stage version performing best, obtaining significant differences in all Boolean problems.

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Reyes Fernández de Bulnes Dde Lima AGalván ERyan C(2024)Feature Encapsulation by Stages in the Regression Domain Using Grammatical EvolutionParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70068-2_7(105-120)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70068-2_7

Index Terms

Feature Encapsulation by Stages Using Grammatical Evolution
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Discrete space search
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression
    2. Machine learning approaches
      1. Bio-inspired approaches
        Genetic programming

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GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2024

2187 pages

ISBN:9798400704956

DOI:10.1145/3638530

Chair:
Xiaodong Li,
Program Chair:
Julia Handl

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Published: 01 August 2024

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GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion

July 14 - 18, 2024

VIC, Melbourne, Australia

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Reyes Fernández de Bulnes Dde Lima AGalván ERyan C(2024)Feature Encapsulation by Stages in the Regression Domain Using Grammatical EvolutionParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70068-2_7(105-120)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70068-2_7

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Probabilistic Grammatical Evolution

Examining the "Best of Both Worlds" of Grammatical Evolution

Structural and nodal mutation in grammatical evolution

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