Differential evolution for neural networks learning enhancement

Evolutionary computation is the name given to a collection of algorithms based on the evolution of a population toward a solution of a certain problem. These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. These algorithms...

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Main Author:	Ismail Wdaa, Abdul Sttar
Format:	Thesis
Language:	English
Published:	2008
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/9489/1/AbdulSttarMFSKSM2008.pdf
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spelling	my-utm-ep.94892018-10-14T07:19:53Z Differential evolution for neural networks learning enhancement 2008-10 Ismail Wdaa, Abdul Sttar QA75 Electronic computers. Computer science Evolutionary computation is the name given to a collection of algorithms based on the evolution of a population toward a solution of a certain problem. These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. These algorithms have widely been used to optimize the learning mechanism of classifiers, particularly on Artificial Neural Network (ANN) Classifier. Major disadvantages of ANN classifier are due to its sluggish convergence and always being trapped at the local minima. To overcome this problem, Differential Evolution (DE) has been used to determine optimal value for ANN parameters such as learning rate and momentum rate and also for weight optimization. In ANN, there are many elements need to be considered, and these include the number of input nodes, hidden nodes, output nodes, learning rate, momentum rate, bias parameter, minimum error and activation/transfer functions. These elements will affect the speed of neural network learning. DE has been applied successfully to improve ANN learning from previous studies. However, there are still some issues on DE approach such as longer training time to produce the output and the usage of complex functions in selection, crossover and mutation calculation. In this study, DE is chosen and applied to feed forward neural network to enhance the learning process and the network learning is validated in terms of convergence rate and classification accuracy. Three programs have developed; Differential Evolution Neural Network (DENN), Genetic Algorithm Neural Network (GANN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on ANN learning using various datasets. The results have revealed that DENN has given quite promising results in terms of convergence rate and smaller errors compared to PSONN and GANN. 2008-10 Thesis http://eprints.utm.my/id/eprint/9489/ http://eprints.utm.my/id/eprint/9489/1/AbdulSttarMFSKSM2008.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:831?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Ismail Wdaa, Abdul Sttar Differential evolution for neural networks learning enhancement
description	Evolutionary computation is the name given to a collection of algorithms based on the evolution of a population toward a solution of a certain problem. These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. These algorithms have widely been used to optimize the learning mechanism of classifiers, particularly on Artificial Neural Network (ANN) Classifier. Major disadvantages of ANN classifier are due to its sluggish convergence and always being trapped at the local minima. To overcome this problem, Differential Evolution (DE) has been used to determine optimal value for ANN parameters such as learning rate and momentum rate and also for weight optimization. In ANN, there are many elements need to be considered, and these include the number of input nodes, hidden nodes, output nodes, learning rate, momentum rate, bias parameter, minimum error and activation/transfer functions. These elements will affect the speed of neural network learning. DE has been applied successfully to improve ANN learning from previous studies. However, there are still some issues on DE approach such as longer training time to produce the output and the usage of complex functions in selection, crossover and mutation calculation. In this study, DE is chosen and applied to feed forward neural network to enhance the learning process and the network learning is validated in terms of convergence rate and classification accuracy. Three programs have developed; Differential Evolution Neural Network (DENN), Genetic Algorithm Neural Network (GANN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on ANN learning using various datasets. The results have revealed that DENN has given quite promising results in terms of convergence rate and smaller errors compared to PSONN and GANN.
format	Thesis
qualification_level	Master's degree
author	Ismail Wdaa, Abdul Sttar
author_facet	Ismail Wdaa, Abdul Sttar
author_sort	Ismail Wdaa, Abdul Sttar
title	Differential evolution for neural networks learning enhancement
title_short	Differential evolution for neural networks learning enhancement
title_full	Differential evolution for neural networks learning enhancement
title_fullStr	Differential evolution for neural networks learning enhancement
title_full_unstemmed	Differential evolution for neural networks learning enhancement
title_sort	differential evolution for neural networks learning enhancement
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2008
url	http://eprints.utm.my/id/eprint/9489/1/AbdulSttarMFSKSM2008.pdf
_version_	1747814738430001152

Differential evolution for neural networks learning enhancement

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