A distributed genetic algorithm improving the generalization behavior of neural networks

被引：0
|
作者：
Branke, J
Kohlmorgen, U
Schmeck, H
机构：
来源：
MACHINE LEARNING: ECML-95 | 1995年 / 912卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Artificial neural networks sometimes generalize poorly to unknown inputs, if they have been trained perfectly on relatively small training sets using standard learning algorithms like e.g. backpropagation. In this paper a distributed genetic algorithm is designed and used to improve the network's generalization capabilities by reducing the number of different weights in the neural network.
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页码：107 / 121
页数：15
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