Search-Intensive Concept Induction
Evolutionary Computation, (1995)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
tested on a simple artificial domain, for the sake of
illustration, and on several complex real-world and
artificial domains, in order to show its power, and to
analyze its behavior under various conditions. The
results obtained so far suggest that genetic search may
be a valuable alternative to logic-based approaches to
learning concepts, when no (or little) a priori
knowledge is available and a very large hypothesis
space has to be explored.
Description
diverse cognitive systems bib
