Improving the parsimony of regression models for an enhanced genetic programming process

Authors:
Alexandru-Ciprian Zăvoianu;Gabriel Kronberger;Michael Kommenda;Daniela Zaharie;Michael Affenzeller
Affiliations:
Department of Computer Science, West University of Timişoara, Romania;Heuristic and Evolutionary Algorithms Laboratory (HEAL), Upper Austrian University of Applied Sciences, Austria;Heuristic and Evolutionary Algorithms Laboratory (HEAL), Upper Austrian University of Applied Sciences, Austria;Department of Computer Science, West University of Timişoara, Romania;Heuristic and Evolutionary Algorithms Laboratory (HEAL), Upper Austrian University of Applied Sciences, Austria
Venue:
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Year:
2011

Citing 7
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Abstract

This research is focused on reducing the average size of the solutions generated by an enhanced GP process without affecting the high predictive accuracy the method exhibits when being applied on a complex, industry proposed, regression problem. As such, the effects the GP enhancements have on bloat have been studied and, finally, a bloat control system based on dynamic depth limiting (DDL) and iterated tournament pruning (ITP) was designed. The resulting bloat control system is able to improve by ≃40% the average GP solution parsimony without impacting average solution accuracy.