Heuristic approaches to task allocation for parallel computing
Heuristic approaches to task allocation for parallel computing
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Exact and Approximate Algorithms for Scheduling Nonidentical Processors
Journal of the ACM (JACM)
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
Direct Chromosome Representation and Advanced Genetic Operators for Production Scheduling
Proceedings of the 5th International Conference on Genetic Algorithms
Using Genetic Algorithms to Schedule Distributed Tasks on a Bus-Based System
Proceedings of the 5th International Conference on Genetic Algorithms
Scheduling by Genetic Local Search with Multi-Step Crossover
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problems
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Task Scheduling with use of Classifier Systems
Selected Papers from AISB Workshop on Evolutionary Computing
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
Performance of Evolutionary Approaches for Parallel Task Scheduling under Different Representations
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
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The present work deals with the problem of allocating a number of non identical tasks in a parallel system. The model assumes that the system consists of a number of identical processors and that only one task may be executed on a processor at a time. All schedules and tasks are nonpreemptive. Graham's [1] well-known list scheduling algorithm (LSA) is contrasted with different evolutionary algorithms (EAs), which differ on the representations and the recombinative approach used. Regarding representation, direct and indirect representation of schedules are used. Concerning recombination, the conventional single crossover per couple (SCPC) and a multiple crossover per couple (MCPC) are used [2]. Outstanding behaviour of evolutionary algorithms when contrasted against LSA was detected. Results are shown and discussed.