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Use of Backpropagation and Differential Evolution Algorithms to Training MLPs
Conference proceeding   Open access

Use of Backpropagation and Differential Evolution Algorithms to Training MLPs

Luiz Carlos Camargo, Hegler Correa Tissot and Aurora Trinidad Ramirez Pozo
2012 31st International Conference of the Chilean Computer Science Society, pp 78-86
Nov 2012
url
https://discovery.ucl.ac.uk/10097653/1/A-Use%20of%20Backpropagation%20and%20Differential%20Evolution%20algorithms%20to%20training%20MLPs.pdfView

Abstract

Artificial Neural Network Artificial neural networks Backpropagation Backpropagation (BP) algorithm Databases Differential Evolution (DE) algorithm Multilayer Perceptron Sociology Statistics Training Vectors
Artificial Neural Networks (ANNs) are often used (trained) to find a general solution in problems where a pattern needs to be extracted, such as data classification. Feedforward (FFNN) is one of the ANN architectures and multilayer perceptron (MLP) is a type of FFNN. Based on gradient descent, backpropagation (BP) is one of the most used algorithms for MLP training. Evolutionary algorithms can be also used to train MLPs, including Differential Evolution (DE) algorithm. In this paper, BP and DE are used to train MLPs and they are both compared in four different approaches: (a) backpropagation, (b) DE with fixed parameter values, (c) DE with adaptive parameter values and (d) a hybrid alternative using both DE+BP algorithms.

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Computer Science, Interdisciplinary Applications
Computer Science, Software Engineering
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