Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Sequential and Parallel Cellular Automata-Based Scheduling Algorithms
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
Generalized Extremal Optimization for Solving Multiprocessor Task Scheduling Problem
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
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We propose a solution of the multiprocessor scheduling problem based on applying a relatively new metaheuristic called Generalized Extremal Optimization (GEO). GEO is inspired by a simple coevolutionary model known as Bak-Sneppen model. The model describes an ecosystem consisting of N species. Evolution in this model is driven by a process in which the weakest species in the ecosystem, together with its nearest neighbors is always forced to mutate. This process shows characteristic of a phenomenon called a punctuated equilibrium which is observed in evolutionary biology. We interpret the multiprocessor scheduling problem in terms of the Bak-Sneppen model and apply the GEO algorithm to solve the problem. We compare GEO algorithm with well-known Simulated Annealing (SA) algorithm. Both algorithms have some similarities which are considered in this paper. Experimental results show that GEO despite of its simplicity outperforms SA algorithm in all range of the scheduling instances.