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Authors: Alessandro Re ; Leonardo Vanneschi and Mauro Castelli

Affiliation: NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisbon and Portugal

ISBN: 978-989-758-384-1

ISSN: 2184-2825

Keyword(s): Genetic Programming, Geometric Semantic Genetic Programming, Machine Learning, Ensembles, Master Algorithm.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Representation Techniques ; Soft Computing

Abstract: This paper presents a proof of concept. It shows that Genetic Programming (GP) can be used as a “universal” machine learning method, that integrates several different algorithms, improving their accuracy. The system we propose, called Universal Genetic Programming (UGP) works by defining an initial population of programs, that contains the models produced by several different machine learning algorithms. The use of elitism allows UGP to return as a final solution the best initial model, in case it is not able to evolve a better one. The use of genetic operators driven by semantic awareness is likely to improve the initial models, by combining and mutating them. On three complex real-life problems, we present experimental evidence that UGP is actually able to improve the models produced by all the studied machine learning algorithms in isolation.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Re, A.; Vanneschi, L. and Castelli, M. (2019). Universal Learning Machine with Genetic Programming. In Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2019) ISBN 978-989-758-384-1 ISSN 2184-2825, pages 115-122. DOI: 10.5220/0007808101150122

@conference{ecta19,
author={Alessandro Re. and Leonardo Vanneschi. and Mauro Castelli.},
title={Universal Learning Machine with Genetic Programming},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2019)},
year={2019},
pages={115-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007808101150122},
isbn={978-989-758-384-1},
issn={2184-2825},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2019)
TI - Universal Learning Machine with Genetic Programming
SN - 978-989-758-384-1
IS - 2184-2825
AU - Re, A.
AU - Vanneschi, L.
AU - Castelli, M.
PY - 2019
SP - 115
EP - 122
DO - 10.5220/0007808101150122

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