Paper Info
Title
CGA: Chaotic Genetic Algorithm for Fuzzy Job Scheduling in Grid Environment
Abstract
We introduce a Chaotic Genetic Algorithm (CGA) to schedule Grid jobs with uncertainties. We adopt a Fuzzy Set based Execution Time (FSET) model to describe uncertain operation time and flexible deadline of Grid jobs. We incorporate chaos into standard Genetic Algorithm (GA) by logistic function, a simple equation involving chaos. A distinguishing feature of our approach is that the convergence of CGA can be controlled automatically by the three famous characteristics of logistic function: convergent, bifurcating, and chaotic. Following this idea, we propose a chaotic mutation operatorbased on the feedback of fitness function that ameliorates GA, in terms of convergent speed and stability. We present an entropy based metrics to evaluate the performance of CGA. Experimental results illustrate the efficiency and stability of the resulting algorithm.
Year
DOI
Venue
2006
10.1007/978-3-540-74377-4_15
computational intelligence and security
Keywords
DocType
Volume
standard genetic algorithm,grid environment,chaotic mutation,fuzzy set,fitness function,ameliorates ga,fuzzy job scheduling,convergent speed,chaotic genetic algorithm,grid job,execution time,logistic function
Conference
4456
ISSN
Citations 
PageRank 
0302-9743
4
0.43
References 
Authors
15
2
Name
Order
Citations
PageRank
Dan Liu1258.89
Yuanda Cao2555.35