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Conference proceeding
Use of Backpropagation and Differential Evolution Algorithms to Training MLPs
2012 31st International Conference of the Chilean Computer Science Society, pp 78-86
Nov 2012
Abstract
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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Details
- Title
- Use of Backpropagation and Differential Evolution Algorithms to Training MLPs
- Creators
- Luiz Carlos Camargo - Universidade Federal do ParanáHegler Correa Tissot - Federal ReserveAurora Trinidad Ramirez Pozo - Dept. de Inf. (INF), Univ. Fed. do Parana (UFPR), Curitiba, Brazil
- Publication Details
- 2012 31st International Conference of the Chilean Computer Science Society, pp 78-86
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000331225900011
- Scopus ID
- 2-s2.0-84893579248
- Other Identifier
- 991021861654704721
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- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Computer Science, Software Engineering