Evolutionary Associative Memories Through Genetic Programming

Abstract

Associative Memories (AMs) are useful devices designed to recall output patterns from input patterns. Each input-output pair forms an association. Thus, AMs store associations among pairs of patterns. An important feature is that since its origins AMs have been manually designed. This way, during the last 50 years about 26 different models and variations have been reported. In this paper, we illustrate how new models of AMs can be automatically generated through Genetic Programming (GP) based methodology. In particular, GP provides a way to successfully facilitate the search for an AM in the form of a computer program. The efficiency of the proposal was conducted by means of two tests based on binary and real-valued patterns. The experimental results show that it is possible to automatically generate AMs that achieve good results for the selected pattern recognition problems. This opens a new research area that allows, for the first time, synthesizing new AMs to solve specific problems.