A Clustering Approach for Evaluation of Slope Stability Based on Genetic Algorithm

Author

Zhao, Sheng-Li ; Liu, Yan ; Liu, Yong-Jian ; Bai, Yong-Bing

Author_Institution

Hebei Agric. Univ., Baoding

Volume

2

fYear

2007

fDate

19-22 Aug. 2007

Firstpage

952

Lastpage

955

Abstract

A clustering method for evaluation of slope stability is developed based on genetic algorithm. By incorporating features of the problem discussed, the corresponding genetic operators such as selection strategy, crossover operator, and mutation operator are designed to promote global search. Computational results show that the GA-based model can avoid the disadvantages of ordinary clustering methods and find the optimum solution easily with no prior knowledge about data´s distributions.

Keywords

genetic algorithms; mathematical operators; pattern classification; pattern clustering; search problems; statistical analysis; unsupervised learning; clustering method; crossover operator; genetic algorithm; genetic operator; global search; mutation operator; selection strategy; slope stability; unsupervised classification; Biological cells; Clustering methods; Cybernetics; Equations; Genetic algorithms; Genetic mutations; Machine learning; Mathematical model; Multidimensional systems; Stability analysis; Clustering analysis; Evaluation of slope stability; Genetic algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2007 International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4244-0973-0

Electronic_ISBN

978-1-4244-0973-0

Type

conf

DOI

10.1109/ICMLC.2007.4370279

Filename

4370279