DocumentCode :
1671737
Title :
A Genetic Scheduling Algorithm Based on Knowledge for Multiprocessor System
Author :
Zhou, Lan ; Shi-xin, Sun
Author_Institution :
Univ. of Electron. Sci. & Technol. of China, Chengdu
fYear :
2007
Firstpage :
900
Lastpage :
904
Abstract :
With the extensive studies of the task scheduling problem, many new methods, especial genetic algorithms, have been introduced into this field. In this paper, we develop a novel genetic algorithm, namely the knowledge-based genetic scheduling (KGS) algorithm with task duplication. KGS is different from the previously proposed genetic algorithms in a number of ways. Unlike the others genetic algorithms, KGS initializes population based on more knowledge to provide itself a better iterative basis. KGS also designs an effective decoding algorithm to get the best schedule scheme for a certain chromosome. In addition, KGS uses the relative precedence constraints other than absolute priorities to determine the schedule order of tasks. Simulation results show that KGS outperforms the previously proposed algorithms in terms of the solution quality and the execution time.
Keywords :
decoding; genetic algorithms; processor scheduling; decoding algorithm; knowledge-based genetic scheduling algorithm; multiprocessor system; task duplication; task scheduling problem; Algorithm design and analysis; Biological cells; Concurrent computing; Genetic algorithms; Iterative algorithms; Iterative decoding; Multiprocessing systems; Processor scheduling; Scheduling algorithm; Sun;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications, Circuits and Systems, 2007. ICCCAS 2007. International Conference on
Conference_Location :
Kokura
Print_ISBN :
978-1-4244-1473-4
Type :
conf
DOI :
10.1109/ICCCAS.2007.4348194
Filename :
4348194
Link To Document :
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