Application of an evolution strategy to the Hopfield model of associative memory
Akira Imada, Ken Araki · 2002
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.