Optimizing Classifiers for Handwritten Digits by Genetic Algorithms
We present the first large real-world application for the neural network optimizing genetic algorithm Enzo. Nets had several thousands links and the training data up to over 200,000 patterns. We evolved nets for a classification task that have an order of magnitude free parameters less than commonly used polynomial classifiers while maintaining the same performance.
To achieve this we implemented some significant enhancements and minor improvements of the original algorithm.
It is also shown how to use Enzo as an efficient tool to create nets satisfying task-specific constraints.
KeywordsGenetic Algorithm Handwritten Digit Polynomial Classifier Handwritten Digit Recognition Salient Pattern
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