 | The ACM Journal of Experimental Algorithmics |
Volume 7, Article 12, 2002
Relational Concept Learning by Cooperative Evolution
by
http://www.jea.acm.org/2002/NeriRelational/
Abstract:
Concept learning is a computationally demanding task that involves searching
large hypothesis spaces containing candidate descriptions. Stochastic search
combined with parallel processing provides a promising approach to dealing
with such a task.
Learning systems based on distributed genetic algorithms (GAs) have proved
able to find concept descriptions as accurate as those found by other
state-of-the-art learning systems, although the GA approach has the drawback of
being more computationally expensive.
We show here how a suitable architectural design, cooperative evolution,
allows the solution of complex applications with GA-based learning systems
while keeping the computational cost acceptable, thanks to the effective
exploitation of distributed computation. A variety of experimental settings
is used and an explanation of the experimental findings is proposed.
Keywords: distributed genetic algorithm, first order logic
Categories: I.2.6 (Computing Methodologies) Learning: Relational Concept Learning; Induction: GA-Based Learning System
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Last updated and validated May 11, 2002, by editor@jea.acm.org