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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Castelli, M. Manzoni, L. Silva, S. Vanneschi, L. |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Informatics, Systems and Communication (D.I.S. Co.), University of Milano-Bicocca, viale Sarca 336, 20126 Milano, Italy (Castelli, M.; Manzoni, L.; Vanneschi, L.) || KDBIO group of INESC-ID Lisboa, Rua Alves Redol, 9, 1000-029 Lisbon, Portugal and an invited researcher at the ECOS group of Centre for Informatics and Systems of the University of Coimbra, Portugal (Silva, S.) |
| 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. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 171815 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469093 |
| e-ISBN | 9781424469116 |
| DOI | 10.1109/CEC.2010.5585925 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-07-18 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training data Training Optimization Drugs Genetic programming Compounds Electronic mail |
| Content Type | Text |
| Resource Type | Article |
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