Title
An Incremental Genetic Algorithm Approach To Multiprocessor Scheduling
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. © 2004 IEEE.
Publication Date
9-1-2004
Publication Title
IEEE Transactions on Parallel and Distributed Systems
Volume
15
Issue
9
Number of Pages
824-834
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/TPDS.2004.38
Copyright Status
Unknown
Socpus ID
4544231492 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/4544231492
STARS Citation
Wu, Annie S.; Yu, Han; Jin, Shiyuan; Lin, Kuo Chi; and Schiavone, Guy, "An Incremental Genetic Algorithm Approach To Multiprocessor Scheduling" (2004). Scopus Export 2000s. 5063.
https://stars.library.ucf.edu/scopus2000/5063