Social programming using functional swarm optimization

The development of mathematical neural networks was based on an analogy with biological neural networks found in nature. Recently there has been a resurgence in research and understanding in self-organizing networks that are based on other metaphors: genetics, immune systems etc. In this paper a new methodology is presented for creating complex adaptive functional networks (CAFN) that are based on the particle swarm social-psychological metaphor. The proposed social programming methodology is based on combining the particle swarm methodology with the group method of data handling and Cartesian programming.