Improving Proximity and Diversity in Multiobjective Evolutionary Algorithms-Reference-Cited by-同舟云学术

Improving Proximity and Diversity in Multiobjective Evolutionary Algorithms

Published:2010 Issue:10 Volume:E93-D Page:2879-2882
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

AHN Chang Wook¹,KIM Yehoon²

Affiliation:

1. School of Information & Communication Engineering, Sungkyunkwan University

2. Department of Electrical Engineering, Samsung Advanced Institute of Technology

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

http://www.jstage.jst.go.jp/article/transinf/E93.D/10/E93.D_10_2879/_pdf

Reference9 articles.

1. [1] C.W. Ahn, Advances in Evolutionary Algorithms: Theory, Design and Practice, Springer, New York, 2006.

2. [2] E. Zitzler, Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications, Doctoral Dissertation ETH 13398, Swiss Federal Inst. of Tech., Zurich, 1999.

3. [3] E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: Improving the performance of the strength pareto evolutionary algorithm,” Technical Report 103, Swiss Federal Inst. of Tech., Zurich, 2001.

4. [4] N. Srinivas and K. Deb, “Multiobjective function optimization using nondominated sorting genetic algorithms,” Evol. Comput., vol.2, no.3, pp.221-248, 1995.

5. A fast and elitist multiobjective genetic algorithm: NSGA-II

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