Lamarckian evolution of associative memory

There has been a lot of research which applies evolutionary techniques to layered neural networks. However, their application to Hopfield neural networks remain few so far. We apply genetic algorithms to a fully connected Hopfield associative memory model. In an earlier paper, we reported that random weight matrices were evolved to store a number of patterns only by means of a simple genetic algorithm (A. Imada and K. Araki, 1995). We propose that the storage capacity can be enlarged by incorporating Lamarckian inheritance to the genetic algorithm.