Sciweavers

Share
IJCAI
1989

Training Feedforward Neural Networks Using Genetic Algorithms

9 years 5 months ago
Training Feedforward Neural Networks Using Genetic Algorithms
Multilayered feedforward neural networks possess a number of properties which make them particularly suited to complex pattern classification problems. However, their application to some realworld problems has been hampered by the lack of a training algonthm which reliably finds a nearly globally optimal set of weights in a relatively short time. Genetic algorithms are a class of optimization procedures which are good at exploring a large and complex space in an intelligent way to find values close to the global optimum. Hence, they are well suited to the problem of training feedforward networks. In this paper, we describe a set of experiments performed on data from a sonar image classification problem. These experiments both 1) illustrate the improvements gained by using a genetic algorithm rather than backpropagation and 2) chronicle the evolution of the performance of the genetic algorithm as we added more and more domain-specific knowledge into it.
David J. Montana, Lawrence Davis
Added 07 Nov 2010
Updated 07 Nov 2010
Type Conference
Year 1989
Where IJCAI
Authors David J. Montana, Lawrence Davis
Comments (0)
books