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 base on combining the Particle Swarm methodology with The Group Method of Data Handling and Cartesian Programming.