Complexity measures for multi-objective symbolic regression

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

3 Zitate (Scopus)

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 Paretofront 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 multiobjective 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.

OriginalspracheEnglisch
TitelComputer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers
Redakteure/-innenFranz Pichler, Roberto Moreno-Díaz, Alexis Quesada-Arencibia
Herausgeber (Verlag)Springer
Seiten409-416
Seitenumfang8
ISBN (Print)9783319273396
DOIs
PublikationsstatusVeröffentlicht - 2015
Veranstaltung15th International Conference on Computer Aided Systems Theory, Eurocast 2015 - Las Palmas, Gran Canaria, Spanien
Dauer: 8 Feb 201513 Feb 2015
http://eurocast2015.fulp.ulpgc.es/

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band9520
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz15th International Conference on Computer Aided Systems Theory, Eurocast 2015
Land/GebietSpanien
OrtLas Palmas, Gran Canaria
Zeitraum08.02.201513.02.2015
Internetadresse

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