David Rogers Research Institute for Advanced Computer Science MS 230-5, NASA Ames Research Center Moffett Field, CA 94035 ABSTRACT A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near-or over-capacity, where the associative-memory behavior of the model breaksdown, the processing performed by the model can be interpreted asthat of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint ofsparse distributed memory and for which the standard formulation ofSDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with'Genetic Algorithms', and a method for improving the capacity of SDM even when used as an associative memory. OVERVIEW This work is the result of studies involving two seemingly separate topics that proved to share a common framework. The fIrst topic, statistical prediction, is the task of associating extremely large perceptual state vectors with future events.

 John Holland's pioneering book Adaptation in Natural and Artificial Systems [1975, 2nd ed. 1992] showed how the evolutionary process can be applied to solve a wide variety of problems using a highly parallel technique that is now called the genetic algorithm. The genetic algorithm transforms a population of individual objects, each with an associated fitness value, into a new generation of the population using the Darwinian principle of reproduction and survival of the fittest and naturally occurring genetic operations such as crossover (recombination) and mutation. Each individual in the population represents a possible solution to a given problem. The genetic algorithm attempts to find a very good or best solution to the problem by genetically breeding the population of individuals. In preparing to use the conventional genetic algorithm operating on fixed-length character strings to solve a problem, the user must 1. determine the representation scheme, Ann Arbor: The University of Michigan Press

We describe an artificial ant colony capable of solving the travelling salesman problem (TSP). Ants of the artificial colony are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph. Computer simulations demonstrate that the artificial ant colony is capable of generating good solutions to both symmetric and asymmetric instances of the TSP. The method is an example, like simulated annealing, neural networks and evolutionary computation, of the successful use of a natural metaphor to design an optimization algorithm.