Abstract
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 Grahamś 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.
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Esquivel, S., Gatica, C., Gallard, R. (2002). Performance of Evolutionary Approaches for Parallel Task Scheduling under Different Representations. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds) Applications of Evolutionary Computing. EvoWorkshops 2002. Lecture Notes in Computer Science, vol 2279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46004-7_5
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DOI: https://doi.org/10.1007/3-540-46004-7_5
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