Affiliations: Dipartimento di Informatica, Università di Torino, Torino, Italy. [email protected] | Dipartimento di Informatica, Università di Torino, Torino, Italy. [email protected]
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.
