Hybrid Genetic Algorithms for Scheduling Partially Ordered Tasks in a Multi-Processor Environment

Authors:
Man Lin;Laurence Tianruo Yang
Affiliations:
-;-
Venue:
RTCSA '99 Proceedings of the Sixth International Conference on Real-Time Computing Systems and Applications
Year:
1999

Citing 0
Cited 8

Genetic-algorithm-based real-time task scheduling with multiple goals

Journal of Systems and Software - Special issue: Computer systems
Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems

Computers and Operations Research
Scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm in heterogeneous multiprocessors system

Computers and Operations Research
Real-time task scheduling by multiobjective genetic algorithm

Journal of Systems and Software
Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems

Computers and Operations Research
A bipartite genetic algorithm for multi-processor task scheduling

International Journal of Parallel Programming
Multilevel static real-time scheduling algorithms using graph partitioning

ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Parallel genetic algorithms for DVS scheduling of distributed embedded systems

HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Scheduling partially ordered tasks in a multiple- processor environment is a very complex combinatorial optimization problem. In this paper, hybrid Genetic Algorithms for the scheduling optimization problem are presented. We _rst present a non-string representation of the solutions for scheduling problems. Then we provide a hybrid mechanism for the choice of genetic operators. The issue of illegal solution is addressed as well. Experimental results for the choice of parameters and the comparison of GA and Tabu search are also presented.