Optimizing a Manufacturing Process
Active from 1998 - 2002
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.
Numerical Optimization With Neuroevolution 2002
Brian Greer, Henri Hakonen, Risto Lahdelma, and Risto Miikkulainen, In Proceedings of the 2002 Congress on Evolutionary Computation, pp. 361-401, Piscataway, NJ 2002. IEEE. Undergraduate Thesis, Department of Computer Sciences, The University of Texas at Aust...