Heuristic approaches to task allocation for parallel computing
Heuristic approaches to task allocation for parallel computing
Performance Evaluation of Scheduling Precedence-Constrained Computations on Message-Passing Systems
IEEE Transactions on Parallel and Distributed Systems
Transparent remote execution in LAHNOS by means of a neural network device
ACM SIGOPS Operating Systems Review
Genetic Scheduling for Parallel Processor Systems: Comparative Studies and Performance Issues
IEEE Transactions on Parallel and Distributed Systems
A comparison of list schedules for parallel processing systems
Communications of the ACM
Conventional and Multirecombinative Evolutionary Algorithms for the Parallel Task Scheduling Problem
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Using Genetic Algorithms to Schedule Distributed Tasks on a Bus-Based System
Proceedings of the 5th International Conference on Genetic Algorithms
On Permutation Representations for Scheduling Problems
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Static task scheduling and grain packing in parallel processing systems
Static task scheduling and grain packing in parallel processing systems
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Bounds on multiprocessing anomalies and related packing algorithms
AFIPS '72 (Spring) Proceedings of the May 16-18, 1972, spring joint computer conference
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Task scheduling is known to be NP-complete in its general form as well as in many restricted cases. Thus to find a near optimal solution in, at most, polynomial time different heuristics were proposed. The basic Graham's task graph model [1] was extended to other list-based priority schedulers [2] where increased levels of communication overhead were included [3]. Evolutionary Algorithms (EAs) have been used in the past to implement the allocation of the components (tasks) of a parallel program to processors [4], [5]. In this paper five evolutionary algorithms are compared. All of them use the conventional Single Crossover Per Couple (SCPC) approach but they differ in what is represented by the chromosome: processor dispatching priorities, tasks priority lists, or both priority policies described in a bipartite chromosome. Chromosome structure, genetic operators, experiments and results are discussed.