LibGA: a user-friendly workbench for order-based genetic algorithm research
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A branch & bound algorithm for the open-shop problem
GO-II Meeting Proceedings of the second international colloquium on Graphs and optimization
Open Shop Scheduling to Minimize Finish Time
Journal of the ACM (JACM)
The selfish gene algorithm: a new evolutionary optimization strategy
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Proceedings of the 5th International Conference on Genetic Algorithms
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In this paper we investigate the use of three evolutionary based heuristics to the open shop scheduling problem. The intractability of this problem is a motivation for the pursuit of heuristics that produce approximate solutions. This work introduces three evolutionary based heuristics, namely, a permutation genetic algorithm, a hybrid genetic algorithm and a selfish gene algorithm, and tests their applicability to the open shop scheduling problem. Several problem instances are used with our evolutionary based algorithms. We compare the results and conclude with some observations and suggestions on the use of evolutionary heuristics for scheduling problems. We also report on the success that our hybrid genetic algorithm has had on one of the large benchmark problem instances: our heuristic has produced a better solution than the current best known solution.