Using evolutionary programming to construct Hopfield neural networks

This paper presents a new method for constructing discrete-time Hopfield neural networks using evolutionary programming. Under constraints of fixed points, limit cycles or iteration sequences, the method simultaneously acquires both the topology and weights for Hopfield neural networks by solving inequalities. It copes with the limitations of the canonical Hopfield learning algorithm. Experimental results are presented which clearly demonstrate the effectiveness of our approach