Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Artificial Intelligence: A Guide to Intelligent Systems
Artificial Intelligence: A Guide to Intelligent Systems
A taxonomy of scheduling in general-purpose distributed computing systems
IEEE Transactions on Software Engineering
Scheduling in a Grid Computing Environment Using Genetic Algorithms
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
An Integrated Approach to Parallel Scheduling Using Gang-Scheduling, Backfilling, and Migration
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
Heuristics for Scheduling Parameter Sweep Applications in Grid Environments
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Optimal Algorithms for Scheduling Divisible Workloads on Heterogeneous Systems
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Sub optimal scheduling in a grid using genetic algorithms
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Genetic Algorithm Based Scheduler for Computational Grids
HPCS '05 Proceedings of the 19th International Symposium on High Performance Computing Systems and Applications
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Genetic algorithm calibration for two objective scheduling parallel jobs on hierarchical grids
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part II
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Evolutionary optimization algorithms contain, due to their heuristic inspiration, many heuristic parameters, which need to be empirically tuned for the algorithm to work most properly. This paper deals with tuning those parameters in situations when ...