An empirical performance evaluation of a parameter-free genetic algorithm for job-shop scheduling problem

The Job-Shop Scheduling Problem (JSSP) is well known as one of the most difficult NP-hard combinatorial optimization problems. Several GA-based approaches have been reported for the JSSP. Among them, there is a parameter-free genetic algorithm (PfGA) for JSSP proposed by Matsui et al., based on an e...

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Bibliographic Details
Published in:2007 IEEE Congress on Evolutionary Computation pp. 3796 - 3803
Main Authors: Matsui, S., Yamada, S.
Format: Conference Proceeding
Language:English
Japanese
Published: IEEE 01.09.2007
Subjects:
Algorithm design and analysis
Biological cells
Decoding
Design engineering
Genetic algorithms
History
Job shop scheduling
Power engineering and energy
Production
Simulated annealing
ISBN:1424413397, 9781424413393
ISSN:1089-778X
Online Access:Get full text
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Summary:The Job-Shop Scheduling Problem (JSSP) is well known as one of the most difficult NP-hard combinatorial optimization problems. Several GA-based approaches have been reported for the JSSP. Among them, there is a parameter-free genetic algorithm (PfGA) for JSSP proposed by Matsui et al., based on an extended version of PfGA, which uses random keys for representing permutation of operations in jobs, and uses a hybrid scheduling for decoding a permutation into a schedule. They reported that their algorithm performs well for typical benchmark problems, but the experiments were limited to a small number of problem instances. This paper shows the results of an empirical performance evaluation of the GA for a wider range of problem instances. The results show that the GA performs well for many problem instances, and the performance can be improved greatly by increasing the number of subpopulations in the parallel distributed version.
ISBN:1424413397
9781424413393
ISSN:1089-778X
DOI:10.1109/CEC.2007.4424965