Application of an evolution strategy to the Hopfield model of associative memory

Abstract

We apply evolutionary computations to Hopfield's neural network model of associative memory. In the Hopfield model, an almost infinite number of combinations of synaptic weights gives a network an associative memory function. Furthermore, there is a trade-off between the storage capacity and the size of the basin of attraction. Therefore, the model can be thought of as a test suite of multi-modal and/or multi-objective function optimizations. As a preliminary stage, we investigate the basic behavior of an associative memory under simple evolutionary processes. In this paper, we present some experiments using an evolution strategy.