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Neural Networks and Genetic Algorithms are two kind of famous
Computational Intelligence Models. They have shown their
ability in many research domains. In this thesis, we combine
these two technologies to solve pattern recognition problems.
In biology, bacteria can change their expression of genes in
order to save energy. Based on this observation, we combine a
new approach called conditional genes with genetic algorithmsto
achieve neural network structure self-organization. We applythis
method to solve numerals recognition.
Kohonen's Self-Organizing Feature Maps can adapt the output
node topologically. We take advantages of this characteristic
to adapt the structure of neural networks dynamically and a
newrecognizing method is developed. After combining with
conditional genes, neural networks can successfully recognize
hand-written numerals.
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