An incremental genetic algorithm approach to multiprocessor scheduling

    Authors

    A. S. Wu; H. Yu; S. Y. Jin; K. C. Lin;G. Schiavone

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    IEEE Trans. Parallel Distrib. Syst.

    Keywords

    genetic algorithm; task scheduling; parallel processing; TASK DUPLICATION; GRAPHS; Computer Science, Theory & Methods; Engineering, Electrical & Electronic

    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.

    Journal Title

    Ieee Transactions on Parallel and Distributed Systems

    Volume

    15

    Issue/Number

    9

    Publication Date

    1-1-2004

    Document Type

    Article

    Language

    English

    First Page

    824

    Last Page

    834

    WOS Identifier

    WOS:000222679300005

    ISSN

    1045-9219

    Share

    COinS