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Abstract

An evolutionary algorithm evolves alternative architectures and parameters for an image classification system. In a preferred embodiment, a learning system is employed, and during the training period of the learning system, the architecture of the learning system is evolved so as to create a learning system that is well suited to the particular classification problem set. In like manner, other parameters of the image classification system are evolved by the evolutionary algorithm, including those that effect image characterization, learning, and classification. An initial set of parameters and architectures are used to create a set of trial classification systems. A number of pre-classified evaluation images are then applied to each system, and each system's resultant classifications for each test case is compared to the proper classification of each test case. Subsequent trial classification systems are evolved based upon the parameters and architecture of the better performing classification systems. The best performing classification system is then selected as the production classification system for classifying new images.

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