Evolutionary learning with a neuromolecular architecture: a biologically motivated approach to computational adaptability

The effectiveness of evolutionary learning depends both on the variation-selection search operations used and on the structure-function relations of the organization to which these operations are applied. Some organizations-in particular those that occur in biology-are more evolution friendly than others. We describe an artificial neuromolecular (ANM) architecture that illustrates the structure-function relationships that underlie evolutionary adaptability and the manner in which these relationships can be represented in computer programs. The ANM system, a brain-like design that combines intra-and interneuronal levels of processing, can be coupled to a variety of pattern recognition-effector control tasks. The capabilities of the model, in particular its adaptability properties, are here illustrated in the context of Chinese character recognition.