Linkage learning through probabilistic expression

Linkage, in the context of genetic algorithms, represents the ability of bu ilding blocks to bind tightly together and thus travel as one under the act ion of the crossover operator. The goal of learning linkage has been intric ately tied with defeating many of the bogeymen of GAs - building block disr uption, inadequate exploration, spurious correlation and any number of othe r perceived stumbling blocks. Recent studies have shown that linkage can be learned in some very simple problems by simultaneously evolving problem re presentations alongside their solutions. This paper extends the applicabili ty of these approaches by tackling their primary nemesis, the race between allelic selection and linkage learning. (C) 2000 Published by Elsevier Scie nce S.A. All rights reserved.