Problem Solving with a Perpetual Evolutionary Learning Architecture

Abstract

The capability of learning in an indefinite amount of time renders biological systems highly adaptable. We have developed a biologically motivated computer model, called the artificial neuromolecular (ANM) system, that demonstrates long-term evolutionary learning capability for complex problem solving. The major elements of the system are neurons whose input-output behavior is controlled by significant internal dynamics. The dynamics are modeled by cellular automata, structured to represent the neuronal cytoskeleton (a subneuronal network found in every neuron). Neurons of this type are linked into a multilayer network that abstracts some features of visual circuitry. Multiple copies of these networks are controlled by neurons with memory manipulation capabilities. The ANM system combines these two types of neurons into a single, closely integrated architecture. The system is educated to perform desired tasks by evolutionary algorithms. These algorithms act at the intraneuronal level to generate a repertoire of neurons with different pattern processing capabilities. They also act at the interneuronal level (through the memory manipulation system) to orchestrate different pattern processing neurons into a group suitable for performing desired tasks. The system has been applied to Chinese character recognition. Experiments were emphasized on long-term evolutionary learning, relearning capability, self-organizing dynamics, malleability, gradual transformability, multidimensional fitness surface, co-evolutionary learning, and cross-level synergy.