The evolution of internal representation

To develop an appropriate internal representation, a deterministic learning algorithm that can adjust not only weights but also the number of adopted hidden nodes is proposed. The key mechanisms are 1. e recruiting mechanism that recruits proper extra hidden nodes, and e reasoning mechanism that prunes potentially irrelevant hidden nodes earning algorithm can make use of external environmental clues to develop an internal representation appropriate for the required mapping. The encoding problem and the parity problem are used to demonstrate the performance of the proposed algorithm. The experimental results are clearly positive.