Universal learning machine with genetic programming

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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.

Original languageEnglish
Title of host publication Proceedings of the 11th International Joint Conference on Computational Intelligence
EditorsJuan Julian Merelo, Jonathan Garibaldi, Alejandro Linares-Barranco, Kurosh Madani, Kevin Warwick, Kevin Warwick
Place of PublicationViena
PublisherSciTePress
Pages115-122
Number of pages8
Volume1
ISBN (Electronic)9789897583841
DOIs
Publication statusPublished - 1 Jan 2019
Event11th International Joint Conference on Computational Intelligence, IJCCI 2019 - Vienna, Austria
Duration: 17 Sep 201919 Sep 2019

Publication series

NameIJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence

Conference

Conference11th International Joint Conference on Computational Intelligence, IJCCI 2019
CountryAustria
CityVienna
Period17/09/1919/09/19

Keywords

  • Ensembles
  • Genetic programming
  • Geometric semantic genetic programming
  • Machine learning
  • Master algorithm

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