Universal Learning Machine with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{DBLP:conf/ijcci/ReVC19,

• author = "Alessandro Re and Leonardo Vanneschi and Mauro Castelli",
• editor = "Juan Julian Merelo Guervos and Jonathan M. Garibaldi and Alejandro Linares-Barranco and Kurosh Madani and Kevin Warwick",
• title = "Universal Learning Machine with Genetic Programming",
• booktitle = "Proceedings of the 11th International Joint Conference on Computational Intelligence, IJCCI 2019",
• year = "2019",
• volume = "1",
• pages = "115--122",
• month = sep # " 17-19",
• address = "Vienna, Austria",
• publisher = "ScitePress",
• keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Machine Learning, Ensembles, Master Algorithm",
• isbn13 = "978-989-758-384-1",
• URL = "https://research.unl.pt/ws/portalfiles/portal/15379541/Universal_Learning_Machine_Genetic_Programming.pdf",
• URL = "https://doi.org/10.5220/0007808101150122",
• DOI = "doi:10.5220/0007808101150122",
• timestamp = "Mon, 15 Jun 2020 01:00:00 +0200",
• biburl = "https://dblp.org/rec/conf/ijcci/ReVC19.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• size = "8 pages",
• abstract = "We 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.",
• notes = "Affiliation: NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisbon and Portugal",

}

Genetic Programming entries for Alessandro Re Leonardo Vanneschi Mauro Castelli

Citations