Generalization 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 equalization, 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, characterized by a high number of features and where the generalization 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. © 2010 IEEE.

Castelli, M., Manzoni, L., Silva, S., Vanneschi, L. (2010). A comparison of the generalization ability of different genetic programming frameworks. In 2010 IEEE Congress on Evolutionary Computation (IEEE CEC 2010), Pilar Sobrevilla, 18-23 July 2010 (pp.1-8). IEEE [10.1109/CEC.2010.5585925].

A comparison of the generalization ability of different genetic programming frameworks

CASTELLI, MAURO;MANZONI, LUCA;VANNESCHI, LEONARDO
2010

Abstract

Generalization 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 equalization, 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, characterized by a high number of features and where the generalization 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. © 2010 IEEE.
Si
paper
Computational Theory and Mathematics; Applied Mathematics
English
2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
978-142446910-9
Castelli, M., Manzoni, L., Silva, S., Vanneschi, L. (2010). A comparison of the generalization ability of different genetic programming frameworks. In 2010 IEEE Congress on Evolutionary Computation (IEEE CEC 2010), Pilar Sobrevilla, 18-23 July 2010 (pp.1-8). IEEE [10.1109/CEC.2010.5585925].
Castelli, M; Manzoni, L; Silva, S; Vanneschi, L
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/60756
Citazioni
  • Scopus 15
  • ???jsp.display-item.citation.isi??? 4
Social impact