کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
395575 | 665992 | 2007 | 22 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters](/preview/png/395575.png)
In this paper we present a comparative study of several methods that combine evolutionary algorithms and local search to optimize multilayer perceptrons: A method that optimizes the architecture and initial weights of multilayer perceptrons; another that searches for training algorithm parameters, and finally, a co-evolutionary algorithm, introduced here, that handles the architecture, the network’s initial weights and the training algorithm parameters. Our aim is to determine how the co-evolutive method can obtain better results from the point of view of running time and classification ability. Experimental results show that the co-evolutionary method obtains similar or better results than the other approaches, requiring far less training epochs and thus, reducing running time.
Journal: Information Sciences - Volume 177, Issue 14, 15 July 2007, Pages 2884–2905