Advances in Fuzzy Systems — Applications and Theory - Vol. 14
AUTOMATIC GENERATION OF NEURAL NETWORK ARCHITECTURE USING EVOLUTIONARY COMPUTATION
by E Vonk (Vrije Univ. Amsterdam), L C Jain (Univ. South Australia) & R P Johnson (Australian Defense Sci. & Tech. Organ.)
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.
Contents:
- Artificial Neural Networks
- Evolutionary Computation
- The Biological Background
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Mathematical Foundations of Genetic Algorithms
- Implementing Gas
- Hybridisation of Evolutionary Computation and Neural Networks
- Using Genetic Programming to Generate Neural Networks
- Using a GA to Optimise the Weights of a Neural Network
- Using a GA with Grammar Encoding to Generate Neural Networks
- Conclusions and Future Directions
Readership: Scientists, engineers, and researchers interested in artificial intelligence and
systems & knowledge engineering.
| 192pp |
Pub. date: Nov 1997 |
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