Learning with the molecular-based hypernetwork model

The hypernetwork model is a hierarchical architecture that has a representation of the molecular, cellular, and organismic levels of biological organization. It influences flow within each level, and through levels, forming dynamic networks of molecular interactions. With its molecular variation-selection learning algorithm, the hypernetwork is able to solve fairly complex tasks such as the (4-10)-input parity task, and the tic-tac-toe endgame problem, with good results. These performance results illustrate the learning capabilities of this model