Genetic sparse distributed memory

Kanerva´s `sparse distributed memory´ (SDM) is a type of self-organizing neural network which is able to extract a statistical summary from large volumes of data as it is being processed online. Genetic algorithms have been used to optimize the `location address space´ which corresponds to the mapping from the input layer to the hidden units in the neural network implementation of the sparse distributed memory. If treated as a global optimization problem, the genetic algorithm will attempt to optimize the sparse distributed memory so as to extract a single best statistical predictor. However, the real objective is to obtain not just a single global optimum, but to extract information about as many local optima as possible, since each local optimum in this particular definition of the search space represents a different and distinct data pattern that correlates with some output in which we may be interested. The implementation details of a genetic sparse distributed memory as well as modified algorithm designed to deal better with multiple data patterns are presented