A multiagent evolutionary algorithm with direct and indirect combined representation for constraint satisfaction problems-Reference-Cited by-同舟云学术

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A multiagent evolutionary algorithm with direct and indirect combined representation for constraint satisfaction problems

Published:2015-08-07 Issue:3 Volume:21 Page:781-793
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Hao Xingxing,Liu Jing

Funder

FN:National Natural Science Foundation of China

FN:Research Fund for the Doctoral Program of Higher Education of China

FN:Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s00500-015-1815-1.pdf

Reference52 articles.

1. Alba E, Dorronsoro B (2005) The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans Evol Comput 9:126–142

2. Alba E, Dorronsoro B (2009) Cellular genetic algorithms. Springer, New York

3. Bruynooghe M (2004) Enhancing a search algorithm to perform intelligent backtracking. Artif Intell 4(3):371–380

4. Byrski A, Kisiel-Dorohinicki M (2014) Memetic computing in selected agent-based evolutionary systems. In: Process of the 28th European conference on modelling and simulation. EUROSIS, Brescia, pp 27–30

5. Byrski A, Dreżewski R, Siwik L, Kisiel-Dorohinicki M (2015) Evolutionary multi-agent systems. Knowl Eng Rev 30(02):171–186

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