IEEE Transactions on Software Engineering
Multiprocessor scheduling in a genetic paradigm
Parallel Computing
Efficient scheduling of arbitrary task graphs to multiprocessors using a parallel genetic algorithm
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Genetic Scheduling for Parallel Processor Systems: Comparative Studies and Performance Issues
IEEE Transactions on Parallel and Distributed Systems
Scheduling Multiprocessor Tasks with Genetic Algorithms
IEEE Transactions on Parallel and Distributed Systems
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
A Genetic Algorithm for Scheduling Tasks in a Real-Time Distributed System
EUROMICRO '98 Proceedings of the 24th Conference on EUROMICRO - Volume 2
An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
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The problem of multiprocessor scheduling consists in finding a schedule for a general task graph to be executed on a multiprocessor system so that the schedule length can be minimized. This scheduling problem is known to be NP-hard (i.e. algorithms solving the problem have exponential time complexity), and methods based on heuristic search have been proposed to obtain optimal and suboptimal solutions. Efficient methods based on genetic algorithms have been developed by (just to name a few) Hou, Ansari, Ren, Wu, Yu, Jin, Schiavone, Corrêa, Ferreira, Reybrend, ..., to solve the multiprocessor scheduling problem. In this paper, we propose various algorithmic improvements for the multiprocessor scheduling problem. Simulation results show that our methods produce solutions closer to optimality than [3, 5] when the number of processors and/or the number of precedence constraints increase.