A comparison of the generalization ability of different genetic programming frameworks

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

@InProceedings{Castelli:2010:cec,

• author = "Mauro Castelli and Luca Manzoni and Sara Silva and Leonardo Vanneschi",
• title = "A comparison of the generalization ability of different genetic programming frameworks",
• booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
• year = "2010",
• address = "Barcelona, Spain",
• month = "18-23 " # jul,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-4244-6910-9",
• abstract = "Generalisation is an important issue in machine learning. In fact, in several applications good results over training data are not as important as good results over unseen data. While this problem was deeply studied in other machine learning techniques, it has become an important issue for genetic programming only in the last few years. In this paper we compare the generalization ability of several different genetic programming frameworks, including some variants of multi-objective genetic programming and operator equalisation, a recently defined bloat free genetic programming system. The test problem used is a hard regression real-life application in the field of drug discovery and development, characterised by a high number of features and where the generalisation ability of the proposed solutions is a crucial issue. The results we obtained show that, at least for the considered problem, multi-optimization is effective in improving genetic programming generalization ability, outperforming all the other methods on test data.",
• DOI = "doi:10.1109/CEC.2010.5585925",
• notes = "WCCI 2010. Also known as \cite{5585925}",

}

Genetic Programming entries for Mauro Castelli Luca Manzoni Sara Silva Leonardo Vanneschi

Citations