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Using a Queue Genetic Algorithm to Evolve Xpilot Control Strategies on a Distributed System

In this paper, we describe a distributed learning system used to evolve a control program for an agent operating in the network game Xpilot. This system, which we refer to as a queue genetic algorithm, is a steady state genetic algorithm that uses stochastic selection and first-in-first-out replacement. We employ it to distribute fitness evaluations over a local network of dissimilar computers. The system made full use of our available computers while evolving successful controller solutions that were comparable to those evolved using a regular generational genetic algorithm.