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Annie S. Wu, Han Yu, Shiyuan Jin, KuoChi Lin, Guy Schiavone, "An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling," IEEE Transactions on Parallel and Distributed Systems, vol. 15, no. 9, pp. 824834, September, 2004.  
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@article{ 10.1109/TPDS.2004.38, author = {Annie S. Wu and Han Yu and Shiyuan Jin and KuoChi Lin and Guy Schiavone}, title = {An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling}, journal ={IEEE Transactions on Parallel and Distributed Systems}, volume = {15}, number = {9}, issn = {10459219}, year = {2004}, pages = {824834}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2004.38}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Parallel and Distributed Systems TI  An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling IS  9 SN  10459219 SP824 EP834 EPD  824834 A1  Annie S. Wu, A1  Han Yu, A1  Shiyuan Jin, A1  KuoChi Lin, A1  Guy Schiavone, PY  2004 KW  Genetic algorithm KW  task scheduling KW  parallel processing. VL  15 JA  IEEE Transactions on Parallel and Distributed Systems ER   
Abstract—We have developed a genetic algorithm (GA) approach to the problem of task scheduling for multiprocessor systems. Our approach requires minimal problem specific information and no problem specific operators or repair mechanisms. Key features of our system include a flexible, adaptive problem representation and an incremental fitness function. Comparison with traditional scheduling methods indicates that the GA is competitive in terms of solution quality if it has sufficient resources to perform its search. Studies in a nonstationary environment show the GA is able to automatically adapt to changing targets.
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