Using genetic algorithms for sparse distributed memory initialization

Abstract

We describe the use of genetic algorithms to initialize a set of hard locations that constitutes the storage space of Sparse Distributed Memory (SDM). SDM is an associative memory technique that uses binary spaces, and relies on close memory items tending to be clustered together, with some level of abstraction. An important factor in the physical implementation of SDM is how many hard locations are used, which greatly affects the memory capacity. It is also dependent on the dimension of the binary space used. For the SDM system to function appropriately, the hard locations should be uniformly distributed over the binary space. We represented a set of hard locations of SDM as population members, and employed GA to search for the best (fittest) distribution of hard locations over the vast binary space. Accordingly, fitness is based on how far each hard location is from all other hard locations, which measures the uniformity of the distribution. The preliminary results are very promising, with the GA significantly outperforming random initialization used in most existing SDM implementations. This use of GA, which is similar to the Michigan approach, differs from the standard approach in that the object of the search is the entire population.