Automatic process control in the metallurgical and chemical industries is difficult for two reasons: (1) the processes are nonlinear, and (2) they change over time. In this project, neuroevolution techniques were developed to deal with both problems, using a bioreactor process as a domain. Symbiotic evolution of neural networks finds a nonlinear controller efficiently, and particle swarm optimization allows it to adapt to changes in the process. The resulting system learns to control the highly nonlinear system off-line, and adapts on-line to process changes while simultaneously avoiding hard operating constraints.