Evolving neurocontrollers using evolutionary programming

Evolutionary programming (EP) is a stochastic optimization technique that can be used to train neural networks. Unlike many training algorithms, EP does not require gradient information, and this facet increases the applicability of the procedure. The current investigation focuses on evolving neurocontrollers for two difficult nonlinear unstable systems. In the first, two separate poles of varying length are mounted on a cart. In the second, two jointed poles of varying length are mounted on a cart. The objective is to bring the systems into balance. The results indicate the suitability for using EP to evolve neurocontrollers for these two systems