Comparing hybrid systems to design and optimize artificial neural networks

被引：0
|
作者：
Castillo, PA [1 ]
Arenas, MG [1 ]
Merelo, JJ [1 ]
Romero, G [1 ]
Rateb, F [1 ]
Prieto, A [1 ]
机构：
[1] Univ Granada, Dept Architecture & Comp Technol, E-18071 Granada, Spain
来源：
GENETIC PROGRAMMING, PROCEEDINGS | 2004年 / 3003卷
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper we conduct a comparative study between hybrid methods to optimize multilayer perceptrons: a model that optimizes the architecture and initial weights of multilayer perceptrons; a parallel approach to optimize the architecture and initial weights of multilayer perceptrons; a method that searches for the parameters of the training algorithm, and an approach for cooperative co-evolutionary optimization of multilayer perceptrons. Obtained results show that a co-evolutionary model obtains similar or better results than specialized approaches, needing much less training epochs and thus using much less simulation time.
引用
收藏
页码：240 / 249
页数：10
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