Since highly complicated interaction dynamics exist, it is in general extremely difficult to design controllers for legged robots. So far various methods have been proposed with the concept of neural circuits, so–called Central Pattern Generators(CPG). In contrast to these approaches in this article we use a polymorphic neural circuit instead, allowing the dynamic change of its properties according to the current situation in real time. To do so, we introduce the concept of neuromodulation with a diffusion–reaction mechanism of neuromodulators. Since there is currently no theory about how such dynamic neural networks can be created, the evolutionary approach is the method of choice to explore the interaction among the neuromodulators, receptors, synapses and neurons. We apply this neural network to the control of a 3–D biped robot which is intrinsically unstable. In this article, we will show our simulation results and provide some interesting points derived from the obtained results.