International Journal of Computational Intelligence Systems

<Previous Article In Issue
Next Article In Issue>
Volume 9, Issue 1, January 2016, Pages 57 - 64

A Novel Mechanism for Efficient the Search Optimization of Genetic Algorithm

Authors
Chen-Fang Tsaiau1204@mail.au.edu.tw
Department of Industrial Management and Enterprise Information, Aletheia University, Taiwan.
Shin-Li Lu*, shinlilu@mail.au.edu.tw
Department of Industrial Management and Enterprise Information, Aletheia University, Taiwan.
*Shin-Li Lu: Aletheia University, 32 Chen-Li Street, Tamsui, New Taipei City 251, Taiwan.
Corresponding Author
Received 25 June 2015, Accepted 16 November 2015, Available Online 1 January 2016.
DOI
10.1080/18756891.2016.1144153How to use a DOI?
Keywords
Genetic algorithm; Adaptive Crossover; Adaptive Mutation
Abstract

This paper proposes a Social Genetic Algorithm (SGA) that includes a transformation function that has ability to improve search efficiency. The SGA is different from the Traditional Genetic Algorithm (TGA) approaches, as it allows refinement of the TGA parameters for the selections of operators in each generation with two functions: optimization of crossover rate and optimization of mutation rate. In this paper, a new function that optimizes gene relationship has been introduced to advance the evolution capability and flexibility of SGA in searching complex and large solution space. Our proposed approach has been evaluated using simulation models. The simulation results have shown that SGA outperforms TGA in improving search efficiency. The contribution of the proposed approach is a dynamic and adaptive methodology, which has ability to improve efficiency.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 1
Pages
57 - 64
Publication Date
2016/01/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1144153How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Chen-Fang Tsai
AU  - Shin-Li Lu
PY  - 2016
DA  - 2016/01/01
TI  - A Novel Mechanism for Efficient the Search Optimization of Genetic Algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 57
EP  - 64
VL  - 9
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1144153
DO  - 10.1080/18756891.2016.1144153
ID  - Tsai2016
ER  -