AutoGE: A Tool for Estimation of Grammatical Evolution Models

Muhammad Ali, Meghana Kshirsagar, Enrique Naredo, Conor Ryan

2021

Abstract

AutoGE (Automatic Grammatical Evolution), a new tool for the estimation of Grammatical Evolution (GE) parameters, is designed to aid users of GE. The tool comprises a rich suite of algorithms to assist in fine tuning BNF grammar to make it adaptable across a wide range of problems. It primarily facilitates the identification of optimal grammar structures, the choice of function sets to achieve improved or existing fitness at a lower computational overhead over the existing GE setups. This research work discusses and reports initial results with one of the key algorithms in AutoGE, Production Rule Pruning, which employs a simple frequency-based approach for identifying less worthy productions. It captures the relationship between production rules and function sets involved in the problem domain to identify optimal grammar structures. Preliminary studies on a set of fourteen standard Genetic Programming benchmark problems in the symbolic regression domain show that the algorithm removes less useful terminals and production rules resulting in individuals with shorter genome lengths. The results depict that the proposed algorithm identifies the optimal grammar structure for the symbolic regression problem domain to be arity-based grammar. It also establishes that the proposed algorithm results in enhanced fitness for some of the benchmark problems.

Paper Citation

in Harvard Style

Ali M., Kshirsagar M., Naredo E. and Ryan C. (2021). AutoGE: A Tool for Estimation of Grammatical Evolution Models.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 1274-1281. DOI: 10.5220/0010393012741281

in Bibtex Style

@conference{icaart21,
author={Muhammad Ali and Meghana Kshirsagar and Enrique Naredo and Conor Ryan},
title={AutoGE: A Tool for Estimation of Grammatical Evolution Models},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={1274-1281},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010393012741281},
isbn={978-989-758-484-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - AutoGE: A Tool for Estimation of Grammatical Evolution Models
SN - 978-989-758-484-8
AU - Ali M.
AU - Kshirsagar M.
AU - Naredo E.
AU - Ryan C.
PY - 2021
SP - 1274
EP - 1281
DO - 10.5220/0010393012741281