## Search-Intensive Concept Induction (1995)

Citations: | 77 - 3 self |

### BibTeX

@MISC{Giordana95search-intensiveconcept,

author = {Attilio Giordana and Filippo Neri},

title = {Search-Intensive Concept Induction},

year = {1995}

}

### Years of Citing Articles

### OpenURL

### Abstract

This paper describes REGAL, a distributed genetic algorithm-based system, designed for learning First Order Logic concept descriptions from examples. The system is a hybrid between the Pittsburgh and the Michigan approaches, as the population constitutes a redundant set of partial concept descriptions, each evolved separately. In order to increase effectiveness, REGAL is specifically tailored to the concept learning task; hence, REGAL is task-dependent, but, on the other hand, domain-independent. The system proved to be particularly robust with respect to parameter setting across a variety of different application domains. REGAL is based on a selection operator, called Universal Suffrage operator, provably allowing the population to asymptotically converge, in average, to an equilibrium state, in which several species coexist. The system is presented both in a serial and in a parallel version, and a new distributed computational model is proposed and discussed. The system has been test...