Grammatical evolution
Abstract
We present grammatical evolution, an evolutionary algorithm that can evolve complete programs in an arbitrary language using a variable-length binary string. The binary genome determines which production rules in a Backus-Naur form grammar definition are used in a genotype-to-phenotype mapping process to a program. We demonstrate how expressions and programs of arbitrary complexity may be evolved and compare its performance to genetic programming.
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The Optimisation module implements random search and probabilistic grammatical evolution.
“Two optimisation strategies are implemented: random search and probabilistic grammatical evolution (O'Neill and Ryan, 2001).”
From Demo Application for the AutoGOAL Framework (2020) · cites this paper as O'Neill and Ryan, 2001
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Moore, J. H., Boczko, E. M., & Summar, M. L. (2005) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2004.10.006 · Cited by 32 · see citing works
Demo Application for the AutoGOAL Framework
Estevez-Velarde, S., Piad-Morffis, A., Gutiérrez, Y., Montoyo, A., Muñoz-Guillena, R., & Almeida Cruz, Y. (2020) · Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations · doi:10.18653/v1/2020.coling-demos.4
- Authors
- O'Neill, M., & Ryan, C.
- Affiliations
- University of Limerick (IE)
- ORCID
- Ryan, C.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2001
- Volume
- 5
- Issue
- 4
- Pages
- 349-358
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2296218809
- Cited by
- 951
- References
- 0
- Citations by year
- 2026: 29 · 2025: 44 · 2024: 32 · 2023: 50 · 2022: 44 · 2021: 60 · 2020: 57 · 2019: 40 · 2018: 36 · 2017: 50 · 2016: 43 · 2015: 36 · 2014: 41 · 2013: 38 · 2012: 32
- FWCI
- 26.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Evolutionary Computation (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, programming, and type systems
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- grammatical evolution, evolutionary algorithms, program synthesis, Backus-Naur form, production rules, genotype-phenotype mapping, genetic programming comparison, software evolution
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