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A stochastic genetic algorithm for dynamic load balancing in distributed systems

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3 Author(s)
Munetomo, M. ; Dept. of Inf. & Data Anal., Hokkaido Univ., Sapporo, Japan ; Takai, N.K. ; Sato, Y.

This paper presents a genetic algorithm (GA) for stochastic environments and its application to dynamic load balancing in distributed systems. We have proposed a stochastic genetic algorithm (StGA) which has an evaluation mechanism for fitness values based on the reinforcement learning in order to adapt to stochastic environments. We apply the StGA to the decision phase of task migration requests in dynamic load balancing, and we realize a task distribution system based on the StGA in a local area network which consists of UNIX workstations

Published in:
Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on  (Volume:4 )

Date of Conference: 22-25 Oct 1995

Page(s):
3795 - 3799 vol.4
Meeting Date :
22 Oct 1995-25 Oct 1995
Print ISBN:
0-7803-2559-1
INSPEC Accession Number:
5138711
Conference Location :
Vancouver, BC
Digital Object Identifier :
10.1109/ICSMC.1995.538379
Product Type:
Conference Publications

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