Neuroevolution techniques have been successful in many sequential decision tasks such as robot control and game playing. This paper aims at establishing whether they can be useful in numerical optimization more generally, by comparing neuroevolution to Linear Programming in a manufacturing optimization domain. It turns out that neuroevolution can learn to compensate for uncertainty in the data and outperform Linear Programming when the number of variables in the problem is small and the required accuracy is low, but the current techniques do not (yet) provide an advantage in problems where many variables must be optimized with high accuracy.

Technical Report TR-01-49, Department of Computer Science, The University of Texas at Austin.

@techreport{greer:ugthesis01, title={Numerical Optimization with Neuroevolution}, author={Brian Greer}, number={TR-01-49}, institution={Department of Computer Science, The University of Texas at Austin}, type={Undergraduate Honors Thesis}, url="http://www.cs.utexas.edu/users/ai-lab?greer:ugthesis01", year={2001} }