A Parallel Genetic Algorithm for the Open-Shop Scheduling Problem Using Deterministic and Random Moves
This paper investigates the use of parallel genetic algorithms in order to solve the open-shop scheduling problem. The method is based on a novel implementation of genetic operators that combines the use of deterministic and random moves. The method is implemented using MPI on a Beowulf cluster. Comparisons using the Taillard benchmarks give favorable results for this algorithm.
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