DocumentCode
1918163
Title
Implementing evolutionary self-organizing maps with the genetic of graph evolution theory
Author
Chang, Maiga ; Heh, Jia-Sheng
Author_Institution
Dept. of Inf. & Comput. Eng., Chung Yuan Christian Univ., Chung-li, Taiwan
Volume
1
fYear
2003
fDate
20-24 July 2003
Firstpage
462
Abstract
This paper analyzes the genetic operations of a new evolution mechanism proposed by us for improving the capability to deal with graph-form solutions in the real world of genetic algorithms based on the theories of GAs and GPs. A prototype of graph evolution with genetic operations is implemented and applied to some graph-related systems with the Irish-student classification data. Evaluation between conventional optimization mechanisms and graph evolution theory is also made for proving the advantage of using graph evolution. Be notable is the graph evolution theory proposed in this paper can cover most applications of GAs and GPs.
Keywords
genetic algorithms; graph theory; learning (artificial intelligence); pattern classification; self-organising feature maps; Irish student classification data; genetic algorithm; genetic operation; genetic programming; graph evolution theory; graph related system; optimization mechanisms; self organizing maps; Algorithm design and analysis; Biological cells; Character generation; Design optimization; Evolutionary computation; Genetic algorithms; Genetic programming; Prototypes; Self organizing feature maps; Tree graphs;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-7898-9
Type
conf
DOI
10.1109/IJCNN.2003.1223390
Filename
1223390
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