In many real world optimization problems, such as resource management and manufacturing, the optimization has to be done under uncertainty. Uncertainty makes the task nonlinear, and standard methods such as Linear Programming do not perform very well. In this project, the aim is to evolve neural networks with ESP to optimize a manufacturing process (of aluminum recycling). The goal is to learn to compensate for the uncertainty, and utilize hidden regularities in the data. With a small number of variables and with accuracy limited to a small number of values, ESP outperforms Linear Programming, but its performance is not significantly better with several highly accurate variables. This result is both a promise and a challenge for future neuroevolution research.