Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Scheduling Multiprocessor Tasks with Genetic Algorithms
IEEE Transactions on Parallel and Distributed Systems
Scheduling multiprocessor job with resource and timing constraintsusing neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A controlled genetic algorithm by fuzzy logic and belief functionsfor job-shop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-based discrete dynamic programming approach for scheduling inFMS environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
PARM: a power-aware message scheduling algorithm for real-time wireless networks
WMuNeP '05 Proceedings of the 1st ACM workshop on Wireless multimedia networking and performance modeling
Scheduling Security-Critical Real-Time Applications on Clusters
IEEE Transactions on Computers
Improving security for periodic tasks in embedded systems through scheduling
ACM Transactions on Embedded Computing Systems (TECS)
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With the exponential growth of time to obtain an optimal solution, the job-shop scheduling problems have been categorized as NP-complete problems. The time complexity makes the exhaustive search for a global optimal schedule infeasible or even impossible. Recently, genetic algorithms have shown the feasibility to solve the job-shop scheduling problems. However, a pure GA-based approach tends to generate illegal schedules due to the crossover and the mutation operators. It is often the case that the gene expression or the genetic operators need to be specially tailored to fit the problem domain or some other schemes may be combined to solve the scheduling problems. This paper presents a GA-based approach with a feasible energy function to generate good-quality schedules. This work concentrates mainly on dynamic real-time scheduling problems with constraint satisfaction. In our work, we design an easy-understood genotype to generate legal schedules and induce that the proposed approach can converge rapidly to address its applicability.