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An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

Authors:

Rong-Long Wang,

Kozo OkazakiAuthors Info & Claims

Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 11, Issue 7

Pages 687 - 694

https://doi.org/10.1007/s00500-006-0131-1

Published: 15 February 2007 Publication History

Abstract

The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

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Gholami JKamankesh MMohammadi SHosseinkhani EAbdi S(2022)Powerful enhanced Jaya algorithm for efficiently optimizing numerical and engineering problemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-06909-z26:11(5315-5333)Online publication date: 1-Jun-2022
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An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

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cover image Soft Computing - A Fusion of Foundations, Methodologies and Applications

Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 11, Issue 7

February 2007

97 pages

ISSN:1432-7643

EISSN:1433-7479

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 15 February 2007

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