Universal Learning Machine with Genetic Programming

Alessandro Re, Leonardo Vanneschi, Mauro Castelli

2019

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.

Paper Citation

in Harvard Style

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 (IJCCI 2019) - Volume 1: ECTA; ISBN 978-989-758-384-1, SciTePress, pages 115-122. DOI: 10.5220/0007808101150122

in Bibtex Style

@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 (IJCCI 2019) - Volume 1: ECTA},
year={2019},
pages={115-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007808101150122},
isbn={978-989-758-384-1},
}

in EndNote Style

TY - CONF

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