Abstract

This paper presents a genetic algorithm (GA) for stochastic environments and its application to dynamic load balancing in distributed systems. We have proposed a stochastic genetic algorithm (StGA) which has an evaluation mechanism for fitness values based on the reinforcement learning in order to adapt to stochastic environments. We apply the StGA to the decision phase of task migration requests in dynamic load balancing, and we realize a task distribution system based on the StGA in a local area network which consists of UNIX workstations.

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