Contemporary Symbolic Regression Methods and their Relative Performance

Contemporary Symbolic Regression Methods and their Relative Performance

29 July 2021

William La Cava

Patryk Orzechowski

Fabrício Olivetti de Francca

Papers citing "Contemporary Symbolic Regression Methods and their Relative Performance"

17 / 117 papers shown

Title
Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data Bogdan Burlacu M. Kommenda G. Kronberger Stephan M. Winkler M. Affenzeller 17 4 0 13 Jun 2022
GSR: A Generalized Symbolic Regression Approach Tony Tohme Dehong Liu K. Youcef-Toumi 51 14 0 31 May 2022
Symbolic Expression Transformer: A Computer Vision Approach for Symbolic Regression Jiachen Li Ye Yuan Hongze Shen 27 7 0 24 May 2022
DNNR: Differential Nearest Neighbors Regression Youssef Nader Leon Sixt Tim Landgraf 26 13 0 17 May 2022
A Trillion Genetic Programming Instructions per Second W. Langdon 14 3 0 06 May 2022
Taylor Genetic Programming for Symbolic Regression Baihe He Qiang Lu Qingyun Yang Jake Luo Zhiguang Wang 16 29 0 28 Apr 2022
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression M. Virgolin Peter A. N. Bosman 17 7 0 26 Apr 2022
Transformation-Interaction-Rational Representation for Symbolic Regression F. O. França 28 8 0 25 Apr 2022
End-to-end symbolic regression with transformers Pierre-Alexandre Kamienny Stéphane dÁscoli Guillaume Lample Franccois Charton 17 161 0 22 Apr 2022
Population Diversity Leads to Short Running Times of Lexicase Selection Thomas Helmuth Johannes Lengler William La Cava 14 7 0 13 Apr 2022
Automated Learning of Interpretable Models with Quantified Uncertainty G. Bomarito P. Leser N. Strauss K. Garbrecht J. D. Hochhalter 31 10 0 12 Apr 2022
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning M. Virgolin Eric Medvet T. Alderliesten Peter A. N. Bosman 12 6 0 05 Apr 2022
Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models E. Sijben T. Alderliesten Peter A. N. Bosman 8 5 0 24 Mar 2022
On genetic programming representations and fitness functions for interpretable dimensionality reduction Thomas Uriot M. Virgolin T. Alderliesten Peter A. N. Bosman 4 9 0 01 Mar 2022
Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression Dazhuang Liu M. Virgolin T. Alderliesten Peter A. N. Bosman 17 12 0 14 Feb 2022
Chaos as an interpretable benchmark for forecasting and data-driven modelling W. Gilpin AI4TS 19 73 0 11 Oct 2021
Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers Patryk Orzechowski J. Moore ELM VLM 17 13 0 14 Jul 2021