We apply evolutionary computations to the Hop eld's neural network model of associative memory. In the model, some of the appropriate con gurations of synaptic weights give the network a function of associative memory. One of our goals is to obtain the distribution of these con gurations in the synaptic weight space. In other words, our aim is to learn a geometry of a tness landscape de ned on the space. For the purpose, we use evolutionary walks to explore the tness landscape in this paper.