Emergence, Evolutionary Algorithms, Neural Networks, Modularity

An emergence of modular connectionist architecture of neural network is studied in which the networks composing the architecture compete to learn the training patterns. An outcome this process is that different modules of networks learn different training patterns and, thus, learn to compute different functions. The goal is achieved by evolutionary algorithms, similarly as in natural evolution.