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 socialpsychological metaphor. The proposed Social Programming methodology is base on combining the Particle Swarm methodology with The Group Method of Data Handling and Cartesian Programming