Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Models of machines and computation for mapping in multicomputers
ACM Computing Surveys (CSUR)
ALPES: a tool for the performance evaluation of parallel programs
Environments and tools for parallel scientific computing
Multiprocessor scheduling in a genetic paradigm
Parallel Computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Parallel Algorithms and Architectures
Parallel Algorithms and Architectures
On Mapping Systolic Algorithms onto the Hypercube
IEEE Transactions on Parallel and Distributed Systems
Mapping Nested Loop Algorithms into Multidimensional Systolic Arrays
IEEE Transactions on Parallel and Distributed Systems
Pipelined Data Parallel Algorithms-II: Design
IEEE Transactions on Parallel and Distributed Systems
A Processor-Time-Minimal Systolic Array for Transitive Closure
IEEE Transactions on Parallel and Distributed Systems
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
Scheduling DAG's for Asynchronous Multiprocessor Execution
IEEE Transactions on Parallel and Distributed Systems
A taxonomy of scheduling in general-purpose distributed computing systems
IEEE Transactions on Software Engineering
Process scheduling using genetic algorithms
SPDP '95 Proceedings of the 7th IEEE Symposium on Parallel and Distributeed Processing
Multiprocessor Clustering for Embedded Systems
Euro-Par '01 Proceedings of the 7th International Euro-Par Conference Manchester on Parallel Processing
An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
Dynamic Real-Time Scheduling for Multi-Processor Tasks Using Genetic Algorithm
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Framework for Task Scheduling in Heterogeneous Distributed Computing Using Genetic Algorithms
Artificial Intelligence Review
PGGA: a predictable and grouped genetic algorithm for job scheduling
Future Generation Computer Systems - Parallel input/output management techniques (PIOMT) in cluster and grid computing
Efficient Techniques for Clustering and Scheduling onto Embedded Multiprocessors
IEEE Transactions on Parallel and Distributed Systems
Proceedings of the conference on Design, automation and test in Europe
Static scheduling techniques for dependent tasks on dynamically reconfigurable devices
Journal of Systems Architecture: the EUROMICRO Journal
A comparison of multiprocessor task scheduling algorithms with communication costs
Computers and Operations Research
Push-Pull: Deterministic Search-Based DAG Scheduling for Heterogeneous Cluster Systems
IEEE Transactions on Parallel and Distributed Systems
Expert Systems with Applications: An International Journal
A performance study of multiprocessor task scheduling algorithms
The Journal of Supercomputing
A new strategy for multiprocessor scheduling of cyclic task graphs
International Journal of High Performance Computing and Networking
Instruction scheduling using evolutionary programming
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
An Evolutionary Approach to Task Graph Scheduling
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Expert Systems with Applications: An International Journal
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
Ant colony optimization for precedence-constrained heterogeneous multiprocessor assignment problem
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Reliability-Oriented Genetic Algorithm for Workflow Applications Using Max-Min Strategy
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
A memetic algorithm for reliability-based dynamic scheduling in heterogeneous computing environments
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
Contributions to the multiprocessor scheduling problem
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
PGGA: A predictable and grouped genetic algorithm for job scheduling
Future Generation Computer Systems - Parallel input/output management techniques (PIOMT) in cluster and grid computing
A bipartite genetic algorithm for multi-processor task scheduling
International Journal of Parallel Programming
Multi-constraint system scheduling using dynamic and delay ant colony system
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Lifetime reliability-aware task allocation and scheduling for MPSoC platforms
Proceedings of the Conference on Design, Automation and Test in Europe
Information Sciences: an International Journal
Future Generation Computer Systems
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Multi-Criteria Job Scheduling in Grid Using an Accelerated Genetic Algorithm
Journal of Grid Computing
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In the multiprocessor scheduling problem, a givenprogram is to be scheduled in a given multiprocessor system such that the program's execution time is minimized. This problem being very hard to solve exactly, many heuristic methods for finding a suboptimal schedule exist. We propose a new combined approach, where a genetic algorithm is improved with the introduction of some knowledge about the scheduling problem represented by the use of a list heuristic in the crossover and mutation genetic operations. This knowledge-augmented genetic approach is empirically compared with a 驴pure驴 genetic algorithm and with a 驴pure驴 list heuristic, both from the literature. Results of the experiments carried out with synthetic instances of the scheduling problem show that our knowledge-augmented algorithm produces much better results in terms of quality of solutions, although being slower in terms of execution time.