An exploration of genetic algorithms for the selection of connection weights in dynamical neural networks

Abstract

Genetic algorithms are used to search for network weights which cause the dynamical network to produce long attractors. Several variations of the genetic algorithm are described, and the search performance is compared to that of the base-line method of randomly selected weights. It is pointed out that dynamical networks support self-sustaining patterns of oscillation which can be initiated by a one-time input strobe. These self-sustaining patterns, or attractor cycles, evolve into a repeating pattern for most combinations of network weights and input strobes. Attractor cycles vary in length and are a function of the particular network weights and the particular strobe. An interesting property of these networks is that a particular set of network weights can produce, or recall, a variety of repeating patterns, where the one that is evoked depends on the triggering strobe. This effectively is the storage of sequential patterns in the form of attractors.<>