Evolutionary optimized forest of regression trees: application in metallurgy

Authors:
Mirosław Kordos;Jerzy Piotrowski;Szymon Bialka;Marcin Blachnik;Slawomir Golak;Tadeusz Wieczorek
Affiliations:
Department of Mathematics and Computer Science, University of Bielsko-Biala, Bielsko-Biala, Poland;Department of Mathematics and Computer Science, University of Bielsko-Biala, Bielsko-Biala, Poland;Department of Mathematics and Computer Science, University of Bielsko-Biala, Bielsko-Biala, Poland;Department of Management and Informatics, Silesian University of Technology, Katowice, Poland;Department of Management and Informatics, Silesian University of Technology, Katowice, Poland;Department of Management and Informatics, Silesian University of Technology, Katowice, Poland
Venue:
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Year:
2012

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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.