Application of genetic algorithm-based intuitionistic fuzzy weighted c-ordered-means algorithm to cluster analysis

2021 ◽  
Author(s):  
R. J. Kuo ◽  
C. K. Chang ◽  
Thi Phuong Quyen Nguyen ◽  
T. W. Liao
Keyword(s):  
Genetic Algorithm ◽  
Cluster Analysis ◽  
Intuitionistic Fuzzy

An enhanced genetic algorithm with new mutation for cluster analysis

2019 ◽  
Vol 34 (3) ◽  
pp. 1355-1392 ◽  
Author(s):  
M. A. El-Shorbagy ◽  
A. Y. Ayoub ◽  
A. A. Mousa ◽  
I. M. El-Desoky
Keyword(s):  
Genetic Algorithm ◽  
Cluster Analysis ◽  
New Mutation

Generation and Classification of Structural Topologies With Genetic Algorithm Speciation

1997 ◽  
Vol 119 (1) ◽  
pp. 127-131 ◽  
Author(s):  
J. W. Duda ◽  
M. J. Jakiela
Keyword(s):  
Genetic Algorithm ◽  
Cluster Analysis ◽  
Genetic Algorithms ◽  
Design Space ◽  
Analysis Techniques ◽  
Statistical Cluster ◽  
The Creation

Extending previous efforts, this article describes how a speciating genetic algorithm is used to distribute subsets of the evolving population of solutions over the design space. This distribution of solutions is analogous to different species exploiting different niches in an ecosystem. In addition to reviewing genetic algorithms with an emphasis on techniques to cause such niche exploitation, we describe how we use statistical cluster analysis techniques to quantify the extent to which a population is speciated and how this measure can be used to probabilistically encourage mating of reasonably similar designs (i.e., intraspecies mating). Results demonstrate the creation of different good designs of characteristically different topology and shape.

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