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

Abstract

 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.