Abstract
We present experiments which show that a genetic algorithm (GA) can effectively search for a set of local feature detectors, which can be used by higher neural network layers to perform an image classification task. Three different methods of encoding hidden unit weights into the GA are presented, including one which co-evolves all the feature detectors in a single chromosome, and two which promote the cooperation of feature detectors by encoding them in their own chromosome. The fitness function measures the classification percentage and confidence of the networks. The three algorithms are all capable of finding a set of feature detectors which allow for 100 percent classification performance, but a novel variant of the cooperative method produces the most consistent, highest confidence classifiers.
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