DocumentCode :
3496133
Title :
Structural optimization of neural network by genetic algorithm with damaged genes
Author :
Takahama, Tetsuyuki ; Sakai, Setsuko
Author_Institution :
Dept. of Intelligent Syst., Hiroshima City Univ., Japan
Volume :
3
fYear :
2002
fDate :
18-22 Nov. 2002
Firstpage :
1211
Abstract :
There are some difficulties in researches on supervised learning using neural networks: difficulty of selection of a proper network structure, and difficulty of interpretation of the hidden units. In this paper, DGGA (Genetic Algorithm with Damaged Genes) is proposed to optimize the network structure of neural networks. DGGA employs real-coded genetic algorithm and introduces the idea of genetic damage. In DGGA, the information of damaged rate is added to each gene. DGGA inactivates the genes that have lower effectiveness using genetic damage. The performance of DGGA for structural optimization is shown by optimizing a simple problem. Also, it is shown that DGGA is an efficient algorithm for structural optimization of neural network by applying DGGA to learning of a logical function.
Keywords :
genetic algorithms; learning (artificial intelligence); neural nets; DGGA algorithm; damaged genes; genetic algorithm; genetic damage; logical function learning; neural network; neural networks; structural optimization; supervised learning; Biological cells; Estimation error; Genetic algorithms; Intelligent systems; Mean square error methods; Neural networks; Optimization methods; Supervised learning; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
Type :
conf
DOI :
10.1109/ICONIP.2002.1202813
Filename :
1202813
Link To Document :
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