Evolutionary optimized forest of regression trees: Application in metallurgy

Abstract

A forest of regression trees is generated, with each tree using a different randomly chosen subset of data. Then the forest is optimized in two ways. First each tree independently by shifting the split points to the left or to the right to compensate for the fact, that the original split points were set up as being optimal only for the given node and not for the whole tree. Then evolutionary algorithms are used to exchange particular tree subnodes between different trees in the forest. This leads to the best single tree, which although may produce not better results than the forest, but can generate comprehensive logical rules that are very important in some practical applications. The system is currently being applied in the optimization of metallurgical processes. © 2012 Springer-Verlag.