Title :
On the configuration of multilayered feedforward networks by an evolutionary process
Author :
Chow, C.R. ; Chu, C.H.
Author_Institution :
Center for Adv. Comput. Studies, Southwestern Louisiana Univ., Lafayette, LA, USA
Abstract :
A learning algorithm based on genetic algorithms to configure multilayered feedforward networks in supervised learning mode is described. The method described lets a population of hidden units compete among themselves and “sell” themselves to be connected to members of another pool of output units. An output unit in this sense therefore represents a team comprising the output unit itself and its connected hidden units. This “team”, of course, defines the architecture of the network. If each output unit can choose for itself how many hidden units it needs to accomplish the classification task, different architectures can be seen to be competing against each other. Experiment results are presented and discussed
Keywords :
feedforward neural nets; genetic algorithms; learning (artificial intelligence); neural net architecture; pattern classification; architecture; classification; configuration; evolutionary process; genetic algorithm; multilayered feedforward networks; supervised learning; Biological cells; Clustering algorithms; Computer networks; Genetic algorithms; Genetic mutations; Genetic programming; Multilayer perceptrons; Neural networks; Neurons;
Conference_Titel :
Circuits and Systems, 1994., Proceedings of the 37th Midwest Symposium on
Conference_Location :
Lafayette, LA
Print_ISBN :
0-7803-2428-5
DOI :
10.1109/MWSCAS.1994.519294
