Natural Niching for Evolving Cooperative Classifiers
Jeffrey D. Horn, Jeffrey D. Horn, David E. Goldberg, David E. Goldberg · The MIT Press eBooks · 1996
An evolutionary classifier, such as a learning classifier system (LCS) or a genetic programming boolean concept learner, must maintain a population of diverse rules that together solve a problem (e.g., classify examples). To maintain "cooperative diversity" while applying a selection operator to the population of rules, as in the Michigan-style LCS, the evolutionary algorithm must incorporate some kind of niching mechanism. The natural way to accomplish niching in an LCS is to force competing rules to share resources (i.e., rewards). The implicit or "natural" niching and speciation induced by such resource sharing, is shown to be robust in the face of severe selective pressure, low population sizes, and overlapping rule coverage. Specifically in this paper we analyze the two-niche (two competing/cooperating rules) case. We find closed form approximations for niche maintenance and niche convergence times, giving us the beginnings of a first predictive model for interacting (cooperating...