Add to ORKGIn this paper, we describe a genetic algorithm (GA) based approach for learning connection weights for an artificial neural network (ANN). We use simulated data sets to compare the GA based approach for learning connection weights against the traditional back-propagation algorithm. Our results indicate that GA based training of ANN has a higher reliability (in terms of over-fitting the training data set) and predictive power than the traditional back-propagation algorithm
This thesis starts with a brief introduction to neural networks and the tuning of neural networks us...
Gradient descent techniques such as back propagation have been used effectively to train neural netw...
This study proposes the use of a modified genetic algorithm (MGA), a global search technique, as a t...
A neural network may be considered as an adaptive system that progressively self-organizes in order ...
Artificial neural networks (ANNs) are new technology emerged from approximate simulation of human br...
Abstract- Artificial Neural Networks have a number of properties which make them psuitable to solve ...
The authors present a technique for reducing the search-space of the genetic algorithm (GA) to impro...
Various schemes for combining genetic algorithms and neural networks have been proposed in recent ye...
Although, the genetic algorithm (GA) has been shown to be a superior neural network (NN) training me...
In the last few years, there have been many works in the area of hybrid neural learning algorithms c...
Abstract: The artificial neural networks (ANN) have proven their efficiency in several applications:...
We present a general and systematic method for neural network design based on the genetic algorithm....
AbstractNeural Networks and Genetic Algorithms are two techniques for optimization and learning, eac...
Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2002-20...
Artificial neural network (ANN) architecture design has been one of the most tedious and difficult t...
This thesis starts with a brief introduction to neural networks and the tuning of neural networks us...
Gradient descent techniques such as back propagation have been used effectively to train neural netw...
This study proposes the use of a modified genetic algorithm (MGA), a global search technique, as a t...
A neural network may be considered as an adaptive system that progressively self-organizes in order ...
Artificial neural networks (ANNs) are new technology emerged from approximate simulation of human br...
Abstract- Artificial Neural Networks have a number of properties which make them psuitable to solve ...
The authors present a technique for reducing the search-space of the genetic algorithm (GA) to impro...
Various schemes for combining genetic algorithms and neural networks have been proposed in recent ye...
Although, the genetic algorithm (GA) has been shown to be a superior neural network (NN) training me...
In the last few years, there have been many works in the area of hybrid neural learning algorithms c...
Abstract: The artificial neural networks (ANN) have proven their efficiency in several applications:...
We present a general and systematic method for neural network design based on the genetic algorithm....
AbstractNeural Networks and Genetic Algorithms are two techniques for optimization and learning, eac...
Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2002-20...
Artificial neural network (ANN) architecture design has been one of the most tedious and difficult t...
This thesis starts with a brief introduction to neural networks and the tuning of neural networks us...
Gradient descent techniques such as back propagation have been used effectively to train neural netw...
This study proposes the use of a modified genetic algorithm (MGA), a global search technique, as a t...