DocumentCode :
2467884
Title :
Evolving Neural Network Ensembles by Fitness Sharing
Author :
Liu, Yong ; Yao, Xin
Author_Institution :
Univ. of Aizu, Fukushima
fYear :
0
fDate :
0-0 0
Firstpage :
3289
Lastpage :
3293
Abstract :
The difference between evolving neural networks and evolving neural network ensembles is that the solution of evolving neural networks is an evolved neural network while the solution of evolving neural network ensemble is an evolved population of neural networks. In the practice of evolving neural network ensemble, it is common that each individual rather the whole population is evaluated. During the evolution, the solution of evolving neural networks would be better and better while it might not be the case for the solution of evolving neural network ensembles. It suggests that the final evolved population might be worse so that it is not wise to choose the final population as a solution. Through experimental studies, this paper gives ideas of how to evolve better populations.
Keywords :
evolutionary computation; learning (artificial intelligence); neural nets; evolutionary learning; evolving neural network ensembles; fitness sharing; hydrid learning system; negative correlation learning; Adaptive systems; Algorithm design and analysis; Computer science; Evolutionary computation; Geology; Humans; Learning systems; Length measurement; Neural networks; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9487-9
Type :
conf
DOI :
10.1109/CEC.2006.1688727
Filename :
1688727
Link To Document :
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