Feature standardisation and coefficient optimisation for effective symbolic regression

作者: Grant Dick , Caitlin A. Owen , Peter A. Whigham

DOI: 10.1145/3377930.3390237

关键词:

摘要: … , genetic programming approaches to symbolic regression … a straightforward model of genetic programming, using feature … symbolic regression. This suggests that the proposed method, …

参考文章(19)
Taras Kowaliw, René Doursat, Bias-variance decomposition in genetic programming Open Mathematics. ,vol. 14, pp. 62- 80 ,(2016) , 10.1515/MATH-2016-0005
Qi Chen, Bing Xue, Lin Shang, Mengjie Zhang, Improving Generalisation of Genetic Programming for Symbolic Regression with Structural Risk Minimisation genetic and evolutionary computation conference. pp. 709- 716 ,(2016) , 10.1145/2908812.2908842
Alison Cozad, Nikolaos V. Sahinidis, A global MINLP approach to symbolic regression Mathematical Programming. ,vol. 170, pp. 97- 119 ,(2018) , 10.1007/S10107-018-1289-X
Leo Breiman, Random Forests Machine Learning archive. ,vol. 45, pp. 5- 32 ,(2001) , 10.1023/A:1010933404324
Michael Kommenda, Bogdan Burlacu, Gabriel Kronberger, Michael Affenzeller, Parameter identification for symbolic regression using nonlinear least squares Genetic Programming and Evolvable Machines. ,vol. 21, pp. 471- 501 ,(2020) , 10.1007/S10710-019-09371-3
Grant Dick, Peter A. Whigham, Controlling Bloat through Parsimonious Elitist Replacement and Spatial Structure Lecture Notes in Computer Science. pp. 13- 24 ,(2013) , 10.1007/978-3-642-37207-0_2
Michael F. Korns, A Baseline Symbolic Regression Algorithm Springer, New York, NY. pp. 117- 137 ,(2013) , 10.1007/978-1-4614-6846-2_9
Qi Chen, Bing Xue, Mengjie Zhang, Generalisation and domain adaptation in GP with gradient descent for symbolic regression congress on evolutionary computation. pp. 1137- 1144 ,(2015) , 10.1109/CEC.2015.7257017
David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams, Learning representations by back-propagating errors Nature. ,vol. 323, pp. 696- 699 ,(1988) , 10.1038/323533A0