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
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.