Automatic Generation Of Neural Network A (advances In Fuzzy Systems)
by Lakhmi C Jain /
1997 / English / PDF
73.5 MB Download
This book describes the application of evolutionary computation in
the automatic generation of a neural network architecture. The
architecture has a significant influence on the performance of the
neural network. It is the usual practice to use trial and error to
find a suitable neural network architecture for a given problem.
The process of trial and error is not only time-consuming but may
not generate an optimal network. The use of evolutionary
computation is a step towards automation in neural network
architecture generation.An overview of the field of evolutionary
computation is presented, together with the biological background
from which the field was inspired. The most commonly used
approaches to a mathematical foundation of the field of genetic
algorithms are given, as well as an overview of the hybridization
between evolutionary computation and neural networks. Experiments
on the implementation of automatic neural network generation using
genetic programming and one using genetic algorithms are described,
and the efficacy of genetic algorithms as a learning algorithm for
a feedforward neural network is also investigated.
This book describes the application of evolutionary computation in
the automatic generation of a neural network architecture. The
architecture has a significant influence on the performance of the
neural network. It is the usual practice to use trial and error to
find a suitable neural network architecture for a given problem.
The process of trial and error is not only time-consuming but may
not generate an optimal network. The use of evolutionary
computation is a step towards automation in neural network
architecture generation.An overview of the field of evolutionary
computation is presented, together with the biological background
from which the field was inspired. The most commonly used
approaches to a mathematical foundation of the field of genetic
algorithms are given, as well as an overview of the hybridization
between evolutionary computation and neural networks. Experiments
on the implementation of automatic neural network generation using
genetic programming and one using genetic algorithms are described,
and the efficacy of genetic algorithms as a learning algorithm for
a feedforward neural network is also investigated.