Automatic Generation of Neural Network Architecture Using Evolutionary Computation

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World Scientific, 1997 - Computers - 182 pages
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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.
 

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Contents

EVOLUTIONARY COMPUTATION
17
THE BIOLOGICAL BACKGROUND
43
MATHEMATICAL FOUNDATIONS OF GENETIC ALGORITHMS
60
IMPLEMENTING GAs
79
HYBRIDISATION OF EVOLUTIONARY COMPUTATION
91
USING GENETIC PROGRAMMING TO GENERATE NEURAL
101
USING A GA TO OPTIMISE THE WEIGHTS OF A NEURAL
114
USING A GA WITH GRAMMAR ENCODING TO GENERATE
131
CONCLUSIONS AND FUTURE DIRECTIONS
169
INDEX
181
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