research-article

Examining the "Best of Both Worlds" of Grammatical Evolution

Authors:

Peter A. Whigham,

James Maclaurin,

Caitlin A. OwenAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1111 - 1118

https://doi.org/10.1145/2739480.2754784

Published: 11 July 2015 Publication History

Abstract

Grammatical Evolution (GE) has a long history in evolutionary computation. Central to the behaviour of GE is the use of a linear representation and grammar to map individuals from search spaces into problem spaces. This genotype to phenotype mapping is often argued as a distinguishing property of GE relative to other techniques, such as context-free grammar genetic programming (CFG-GP). Since its initial description, GE research has attempted to incorporate information from the grammar into crossover, mutation, and individual initialisation, blurring the distinction between genotype and phenotype and creating GE variants closer to CFG-GP. This is argued to provide GE with the "best of both worlds", allowing degrees of grammatical bias to be introduced into operators to best suit the given problem. This paper examines the behaviour of three grammar-based search methods on several problems from previous GE research. It is shown that, unlike CFG-GP, the performance of "pure" GE on the examined problems closely resembles that of random search. The results suggest that further work is required to determine the cases where the "best of both worlds" of GE are required over a straight CFG-GP approach.

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Mégane JLourenço NMachado PSchweim DSilva SPaquete L(2023)The Influence of Probabilistic Grammars on EvolutionProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3590706(611-614)Online publication date: 15-Jul-2023
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Index Terms

Examining the "Best of Both Worlds" of Grammatical Evolution
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

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Published: 11 July 2015

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GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

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GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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https://dl.acm.org/doi/10.1145/3583133.3596315
Mégane JLourenço NMachado PSchweim DSilva SPaquete L(2023)The Influence of Probabilistic Grammars on EvolutionProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3590706(611-614)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3590706
Fonseca APoças DSilva SPaquete L(2023)Comparing the expressive power of Strongly-Typed and Grammar-Guided Genetic ProgrammingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590507(1100-1108)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590507
Tiso SCarvalho PLourenço NMachado PSilva SPaquete L(2023)Biological insights on grammar-structured mutations improve fitness and diversityProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590472(558-567)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590472
Carvalho PMégane JLourenço NMachado P(2023)Context Matters: Adaptive Mutation for GrammarsGenetic Programming10.1007/978-3-031-29573-7_8(117-132)Online publication date: 29-Mar-2023
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Ingelse LFonseca A(2023)Domain-Aware Feature Learning with Grammar-Guided Genetic ProgrammingGenetic Programming10.1007/978-3-031-29573-7_15(227-243)Online publication date: 29-Mar-2023
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Espada GIngelse LCanelas PBarbosa PFonseca AScholz BKameyama Y(2022)Data Types as a More Ergonomic Frontend for Grammar-Guided Genetic ProgrammingProceedings of the 21st ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences10.1145/3564719.3568697(86-94)Online publication date: 29-Nov-2022
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Dick GWhigham PWagner M(2022)Initialisation and grammar design in grammar-guided evolutionary computationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3529051(534-537)Online publication date: 9-Jul-2022
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Mégane JLourenço NMachado PWagner M(2022)Co-evolutionary probabilistic structured grammatical evolutionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528833(991-999)Online publication date: 8-Jul-2022
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Megane JLourenco NMachado P(2022)Probabilistic Structured Grammatical Evolution2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870397(1-9)Online publication date: 18-Jul-2022
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