Scheduling precedence graphs in systems with interprocessor communication times
SIAM Journal on Computing
IEEE Transactions on Software Engineering
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
IEEE Transactions on Parallel and Distributed Systems
Multiprocessor scheduling in a genetic paradigm
Parallel Computing
On Exploiting Task Duplication in Parallel Program Scheduling
IEEE Transactions on Parallel and Distributed Systems
An evolutionary approach to multiprocessor scheduling of dependent tasks
Future Generation Computer Systems - Special issue: Bio-inspired solutions to parallel processing problems
Multiprocessor scheduling using mean-field annealing
Future Generation Computer Systems - Special issue: Bio-inspired solutions to parallel processing problems
Scheduling Multiprocessor Tasks with Genetic Algorithms
IEEE Transactions on Parallel and Distributed Systems
Dynamic Task Scheduling Using Online Optimization
IEEE Transactions on Parallel and Distributed Systems
Observations on Using Genetic Algorithms for Dynamic Load-Balancing
IEEE Transactions on Parallel and Distributed Systems
An Efficient Adaptive Scheduling Scheme for Distributed Memory Multicomputers
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Hypertool: A Programming Aid for Message-Passing Systems
IEEE Transactions on Parallel and Distributed Systems
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors
IEEE Transactions on Parallel and Distributed Systems
Hybrid Genetic Algorithms for Scheduling Partially Ordered Tasks in a Multi-Processor Environment
RTCSA '99 Proceedings of the Sixth International Conference on Real-Time Computing Systems and Applications
Process scheduling using genetic algorithms
SPDP '95 Proceedings of the 7th IEEE Symposium on Parallel and Distributeed Processing
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
Dynamic Task Scheduling using Genetic Algorithms for Heterogeneous Distributed Computing
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
Performance-Driven Processor Allocation
IEEE Transactions on Parallel and Distributed Systems
ACM SIGBED Review - Special issue: IEEE RTAS 2005 work-in-progress
Evolutionary Multiprocessor Task Scheduling
PARELEC '06 Proceedings of the international symposium on Parallel Computing in Electrical Engineering
A comparison of multiprocessor task scheduling algorithms with communication costs
Computers and Operations Research
Practical Multiprocessor Scheduling Algorithms for Efficient Parallel Processing
IEEE Transactions on Computers
A Hybrid Particle Swarm Optimization Approach for Scheduling Flow-Shops with Multiprocessor Tasks
ICISS '08 Proceedings of the 2008 International Conference on Information Science and Security
A hybrid multiprocessor task scheduling method based on immune genetic algorithm
Proceedings of the 5th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness
Information Sciences: an International Journal
Task graph pre-scheduling, using Nash equilibrium in game theory
The Journal of Supercomputing
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Until now, several methods have been presented to optimally solve the multiprocessor task scheduling problem that is an NP-hard one. In this paper, a genetic-based algorithm has been presented to solve this problem with better results in comparison with related methods. The proposed method is a bipartite algorithm in a way that each part is based on different genetic schemes, such as genome presentation and genetic operators. In the first part, it uses a genetic method to find an adequate sequence of tasks and in the second one, it finds the best match processors. To evaluate the proposed method, we applied it on several benchmarks and the results were compared with well known algorithms. The experimental results were satisfactory and in most cases the presented method had a better makespan with at least 10% less iterations compared to related works.