Genetic Algorithms have been gaining much interest since the early 1970\u27s and have intrigued people from the fields of machine learning, artificial intelligence, neural networks and operations research. This paper describes the approach of genetic algorithms applied to neural networks. The experiments were conducted using various functions such as XOR,AND,SINE and different network sizes. Based on the experimental data, we concluded that for small network architectures represented by the functions (SINE,ENCODE,etc), genetic algorithms were not effective and the desired results were not achieved within a reasonable period of time
This paper studies several applications of genetic algorithms (GAs) within the neural networks field...
Neural networks and genetic algorithms are the two sophisticated machine learning techniques present...
Abstract. Genetic algorithms are adaptive search techniques which have been used to learn high-perfo...
The Genetic Algorithm is a popular optimization technique which is bio-inspired and is based on the ...
This book provides a unified framework that describes how genetic learning can be used to design pat...
Neural networks and genetic algorithms are the two sophisticated machine learning techniques present...
This paper provides a review on current developments in genetic algorithms. The discussion includes ...
The design of Artificial Neural Networks by Genetic Algorithm is useful in terms of (1) automating a...
this article we assume that the reader is familiar with the basic ideas of neural networks but perha...
Genetic algorithms are most commonly applied to neural networks to determine their architecture or l...
This thesis starts with a brief introduction to neural networks and the tuning of neural networks us...
The genetic algorithm (GA) and its variants have been used in a wide variety of fields by the scient...
Background Appropriate definition of neural network architecture prior to data analysis is crucial f...
A genetic algorithm (GA) is a search and optimization method developed by mimicking the evolutionary...
Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to le...
This paper studies several applications of genetic algorithms (GAs) within the neural networks field...
Neural networks and genetic algorithms are the two sophisticated machine learning techniques present...
Abstract. Genetic algorithms are adaptive search techniques which have been used to learn high-perfo...
The Genetic Algorithm is a popular optimization technique which is bio-inspired and is based on the ...
This book provides a unified framework that describes how genetic learning can be used to design pat...
Neural networks and genetic algorithms are the two sophisticated machine learning techniques present...
This paper provides a review on current developments in genetic algorithms. The discussion includes ...
The design of Artificial Neural Networks by Genetic Algorithm is useful in terms of (1) automating a...
this article we assume that the reader is familiar with the basic ideas of neural networks but perha...
Genetic algorithms are most commonly applied to neural networks to determine their architecture or l...
This thesis starts with a brief introduction to neural networks and the tuning of neural networks us...
The genetic algorithm (GA) and its variants have been used in a wide variety of fields by the scient...
Background Appropriate definition of neural network architecture prior to data analysis is crucial f...
A genetic algorithm (GA) is a search and optimization method developed by mimicking the evolutionary...
Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to le...
This paper studies several applications of genetic algorithms (GAs) within the neural networks field...
Neural networks and genetic algorithms are the two sophisticated machine learning techniques present...
Abstract. Genetic algorithms are adaptive search techniques which have been used to learn high-perfo...