Structuring pattern generalization through evolutionary techniques

Abstract

The capabilities built into a processing network control the manner in which it generalizes from a training set and therefore how it groups environmental patterns. These capabilities are developed through learning, ultimately evolutionary learning, and therefore have an objective basis in so far as the grouping tendencies afford a selective advantage. But for this development to occur it is necessary that the processing network in fact be able to evolve grouping tendencies that reflect selective pressures. The extent to which this is possible depends on how wide a variety of grouping dynamics the processing network can support (its dynamic richness) and on whether its structure-function gradualism (evolutionary friendliness) is sufficient to provide access to these grouping responses through a variation-selection process. We describe a “softened” cellular automaton model that illustrates how different grouping responses can be evolved in cases simple enough to examine the entire test set.