Cooperative Genetic Algorithm for Optimization Problems in Distributed Computer Systems

In the proposed algorithm, several single population genetic algorithms with diierent cross-over and mutation parameters are run as a set of processes that cooperate periodically and exchange information to solve the problem ef-ciently. The algorithm is less stochastic than the standard genetic algorithm and a distributed implementation is appropriate for application to large scale problems. In particular , we apply it to the static task assignment problem and suggest modiications to solve other optimization problems in distributed computer systems. Preliminary experiments with fairly large-sized problems of allocating 50 tasks among 16 processors indicate that the cooperative algorithm implemented on a network of workstations quickly nds better solutions than those obtained by a standard genetic algorithm. To conclusively show that better solutions are obtained, extensive experiments have to be performed. A distributed implementation of the algorithm is highly suited for such experimentation.