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Computer Science > Machine Learning

arXiv:2109.00238 (cs)
[Submitted on 1 Sep 2021]

Title:Complexity Measures for Multi-objective Symbolic Regression

Authors:Michael Kommenda, Andreas Beham, Michael Affenzeller, Gabriel Kronberger
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Abstract:Multi-objective symbolic regression has the advantage that while the accuracy of the learned models is maximized, the complexity is automatically adapted and need not be specified a-priori. The result of the optimization is not a single solution anymore, but a whole Pareto-front describing the trade-off between accuracy and complexity. In this contribution we study which complexity measures are most appropriately used in symbolic regression when performing multi- objective optimization with NSGA-II. Furthermore, we present a novel complexity measure that includes semantic information based on the function symbols occurring in the models and test its effects on several benchmark datasets. Results comparing multiple complexity measures are presented in terms of the achieved accuracy and model length to illustrate how the search direction of the algorithm is affected.
Comments: International Conference on Computer Aided Systems Theory, Eurocast 2015, pp 409-416
Subjects: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:2109.00238 [cs.LG]
  (or arXiv:2109.00238v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.00238
Journal reference: In: Moreno-Díaz R. et al (eds) Computer Aided Systems Theory, EUROCAST 2015. Lecture Notes in Computer Science, vol 9520 (2015)
Related DOI: https://doi.org/10.1007/978-3-319-27340-2_51

Submission history

From: Michael Kommenda [view email] [via Michaela Beneder as proxy]
[v1] Wed, 1 Sep 2021 08:22:41 UTC (443 KB)
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Michael Kommenda
Andreas Beham
Michael Affenzeller
Gabriel Kronberger
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