Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Biases in the crossover landscape
Proceedings of the third international conference on Genetic algorithms
Sizing populations for serial and parallel genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Using genetic algorithms to solve NP-complete problems
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An introduction to genetic algorithms
An introduction to genetic algorithms
Exact and Approximate Algorithms for Scheduling Nonidentical Processors
Journal of the ACM (JACM)
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Using Genetic Algorithms to Schedule Distributed Tasks on a Bus-Based System
Proceedings of the 5th International Conference on Genetic Algorithms
An Accurate and Efficient Parallel Genetic Algorithm to Schedule Tasks on a Cluster
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
A performance study of multiprocessor task scheduling algorithms
The Journal of Supercomputing
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Recent breakthroughs in the mathematical estimation of parallel genetic algorithm parameters are applied to the NP-complete problem of scheduling multiple tasks on a cluster of computers connected by a shared bus. Numerous adjustments to the original method of parameter estimation were made in order to accurately reflect differences in the problem model. The parallel scheduler used m-ary encoding and included a shared communication bus constraint. Fitness was an indirect computation requiring an evaluation of the meaning and implications (i.e., effect on communication time) of the encoding. The degree of correctness was defined as the "nearness" to the optimal schedule that could be obtained in a limited amount of time. Experiments reveal that the parallel scheduling algorithm developed very accurate schedules when the modified parameter guidelines were used. This article describes the scheduling problem, the parallel genetic scheduler, the adjustments made to the mathematical estimations, the quality of the schedules that were obtained, and the accuracy of the schedules compared to mathematically predicted expected values.