DocumentCode :
2309579
Title :
SOM-based optimization
Author :
Su, Mu-Chun ; Zhao, Yu-Xiang ; Lee, Jonathan
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Taiwan
Volume :
1
fYear :
2004
fDate :
25-29 July 2004
Lastpage :
786
Abstract :
A new approach to optimization problems based on the self-organizing feature maps is proposed. We name the new optimization algorithm the SOM-based optimization (SOMO) algorithm. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited. An additional advantage of the algorithm is that the outputs of the neural network allow us to transform a multi-dimensional fitness landscape into a three-dimensional projected fitness landscape. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm.
Keywords :
genetic algorithms; self-organising feature maps; genetic algorithm; multidimensional fitness landscape; neural network; optimization algorithm; self-organizing feature maps; Biological neural networks; Brain modeling; Clustering algorithms; Computational modeling; Computer science; Evolutionary computation; Genetic algorithms; Genetic mutations; Genetic programming; Particle swarm optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-8359-1
Type :
conf
DOI :
10.1109/IJCNN.2004.1380019
Filename :
1380019
Link To Document :
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