Feature Extraction and Classification by Genetic Programming

This paper explores the use of genetic programming for constructing vision systems. A two-stage approach is used, with separate evolution of the feature extraction and classification stages. The strategy taken for the classifier is to evolve a set of partial solutions, each of which works for a single class. It is found that this approach is significantly faster than conventional genetic programming, and frequently results in a better classifier. The effectiveness of the approach is explored on three classification problems.

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