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
Link To Document :
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