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Authors: Senthil Murugan 1 ; Enrique Naredo 1 ; Douglas Mota Dias 2 ; 1 ; Conor Ryan 1 ; Flaviano Godinez 3 and James Vincent Patten 1

Affiliations: 1 University of Limerick, Limerick, Ireland ; 2 Rio de Janeiro State University, Rio de Janeiro, Brazil ; 3 Universidad Autónoma de Guerrero, Mexico

Keyword(s): Genetic Programming, Novelty Search, Classification.

Abstract: The trend in recent years of the scientific community on solving a wide range of problems through Artificial Intelligence has highlighted the benefits of open-ended search algorithms. In this paper we apply a probabilistic version for a divergent search algorithm in combination of a strategy to reduce the number of evaluations and computational effort by gathering the population from a Genetic Programming algorithm into groups and pruning the worst groups each certain number of generations. The combination proposed has shown encouraging results against a standard GP implementation on three binary classification problems, where the time taken to run an experiment is significantly reduced to only 5% of the total time from the standard approach while still maintaining, and indeed exceeding in the experimental results.

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Paper citation in several formats:
Murugan, S.; Naredo, E.; Dias, D.; Ryan, C.; Godinez, F. and Patten, J. (2022). A Hierarchical Probabilistic Divergent Search Applied to a Binary Classification. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 345-353. DOI: 10.5220/0010841900003116

@conference{icaart22,
author={Senthil Murugan. and Enrique Naredo. and Douglas Mota Dias. and Conor Ryan. and Flaviano Godinez. and James Vincent Patten.},
title={A Hierarchical Probabilistic Divergent Search Applied to a Binary Classification},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={345-353},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010841900003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - A Hierarchical Probabilistic Divergent Search Applied to a Binary Classification
SN - 978-989-758-547-0
IS - 2184-433X
AU - Murugan, S.
AU - Naredo, E.
AU - Dias, D.
AU - Ryan, C.
AU - Godinez, F.
AU - Patten, J.
PY - 2022
SP - 345
EP - 353
DO - 10.5220/0010841900003116
PB - SciTePress

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