PonyGE2: Grammatical Evolution in Python
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Abstract (arXiv)
Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal grammar is used in the genotype to phenotype mapping process. PonyGE2 is an open source implementation of GE in Python, developed at UCD's Natural Computing Research and Applications group. It is intended as an advertisement and a starting-point for those new to GE, a reference for students and researchers, a rapid-prototyping medium for our own experiments, and a Python workout. As well as providing the characteristic genotype to phenotype mapping of GE, a search algorithm engine is also provided. A number of sample problems and tutorials on how to use and adapt PonyGE2 have been developed.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Mar 2017.
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Citing papers per year: 2022: 3; 2023: 2; 2024: 1; 2025: 1.
7 papers.
- 2022Data types as a more ergonomic frontend for Grammar-Guided Genetic Programmingcited by 3
- 2023Discovering Graph Generation Algorithmscited by 1
- 2022Iterative Genetic Improvement: Scaling Stochastic Program Synthesiscited by 0
- 2022Rule-based Evolutionary Bayesian Learningcited by 0
- 2023Automatic Design of Semantic Similarity Ensembles Using Grammatical Evolutioncited by 0
- 2024Analysing the Needs of Homeless People Using Feature Selection and Mining Association Rulescited by 0
- 2025Model Discovery with Grammatical Evolution. An Experiment with Prime Numberscited by 0
- 2025Model Discovery with Grammatical Evolution. An Experiment with Prime Numbers18 May 2025
- 2024Analysing the Needs of Homeless People Using Feature Selection and Mining Association Rules15 Jan 2024
- 2023Automatic Design of Semantic Similarity Ensembles Using Grammatical Evolution3 Jul 2023
- 2023Discovering Graph Generation Algorithms25 Apr 2023
- 2022Data types as a more ergonomic frontend for Grammar-Guided Genetic Programming10 Oct 2022
- 2022Rule-based Evolutionary Bayesian Learning28 Feb 2022
- 2022Iterative Genetic Improvement: Scaling Stochastic Program Synthesis26 Feb 2022