Neural Networks as a Guide to Optimization

Abstract

Applying genetic learning to parameter optimization in the middle game is so heavily penalized by its excessive demand of resources that a more efficient approach had to be searched for. This was found in a neural-network approach with an efficiency far beyond that of genetic learning. No great penalty seems to be incurred in effectiveness. It is concluded that tuning parameters by algorithm for the chess middle game now is feasible.