This paper presents a new approach for the design of feature-extracting recognition networks that do not require expert
knowledge in the application domain. Feature-Extracting Recognition Networks (FERNs) are composed of
interconnected functional nodes (feurons), which serve as feature extractors, and are followed by a subnetwork of
traditional neural nodes (neurons) that act as classifiers. A concurrent evolutionary process (CEP) is used to search the
space of feature extractors and neural networks in order to obtain an optimal recognition network that simultaneously
performs feature extraction and recognition. By constraining the hill-climbing search functionality of the CEP on specific
parts of the solution space, i.e., individually limiting the evolution of feature extractors and neural networks, it was
demonstrated that concurrent evolution is a necessary component of the system. Application of this approach to a
handwritten digit recognition task illustrates that the proposed methodology is capable of producing recognition
networks that perform in-line with other methods without the need for expert knowledge in image processing.
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