Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
A CAD tool for the optimization of video signal processor architectures
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Datapath synthesis using a problem-space genetic algorithm
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Energy aware DAG scheduling on heterogeneous systems
Cluster Computing
Dynamic task partition for video decoding on heterogeneous dual-core platforms
ACM Transactions on Embedded Computing Systems (TECS) - Special section on ESTIMedia'12, LCTES'11, rigorous embedded systems design, and multiprocessor system-on-chip for cyber-physical systems
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We propose a novel stochastic approach for the problem of multiprocessor scheduling and allocation under timing and resource constraints using an evolutionary algorithm (EA). For composite schemes of DSP algorithms a compact problem encoding has been developed with emphasis on the allocation / binding part of the problem as well as an efficient problem transformation-decoding scheme in order to avoid infeasible solutions and therefore time consuming repair mechanisms. Thus, the algorithm is able to handle even large size problems within moderate computation time. Simulation results comparing the proposed EA with optimal results provided by mixed integer linear programming (MILP) show, that the EA is suitable to achieve the same or similar results but in much less time as problem size increases.