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.
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Zăvoianu, AC., Kronberger, G., Kommenda, M., Zaharie, D., Affenzeller, M. (2012). Improving the Parsimony of Regression Models for an Enhanced Genetic Programming Process. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_34
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DOI: https://doi.org/10.1007/978-3-642-27549-4_34
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