Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Giordana, A. | Lo Bello, G.
Affiliations: Dipartimento di Informatica, Università di Torino, Torino, Italy. giordana@di.unito.it | Dipartimento di Informatica, Università di Torino, Torino, Italy. lobello@di.unito.it
Note: [] Address for correspondence: Dipartimento di Informatica, Università di Torino, Torino, Italy
Note: [] Address for correspondence: Dipartimento di Informatica, Università di Torino, Torino, Italy
Abstract: Genetic Algorithms have been proposed by many authors for Machine Learning tasks. In fact, they are appealing for several different reasons, such as the flexibility, the great exploration power, and the possibility of exploiting parallel processing. Nevertheless, it is still controversial whether the genetic approach can really provide effective solutions to learning tasks, in comparison to other algorithms based on classical search strategies. In this paper we try to clarify this point and we overview the work done with respect to the task of learning classification programs from examples. The state of the art emerging from our analysis suggests that the genetic approach can be a valuable alternative to classical approaches, even if further investigation is necessary in order to come to a final conclusion.
Keywords: Machine learning, Concept learning, Genetic Algorithms
DOI: 10.3233/FI-1998-35123409
Journal: Fundamenta Informaticae, vol. 35, no. 1-4, pp. 163-177, 1998
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl