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