9 citations · 12 across the 3 of their papers we have counts for
3 papers
cs.NE2017★ 1 cited
Learning Linear Feature Space Transformations in Symbolic Regression
Jan Žegklitz, Petr Pošík
We propose a new type of leaf node for use in Symbolic Regression (SR) that performs linear combinations of feature variables (LCF). These nodes can be handled in three different m…
cs.LG2017★ 9 cited
Symbolic Regression Algorithms with Built-in Linear Regression
Jan Žegklitz, Petr Pošík
Recently, several algorithms for symbolic regression (SR) emerged which employ a form of multiple linear regression (LR) to produce generalized linear models. The use of LR allows…
cs.NE2015★ 2 cited
Model Selection and Overfitting in Genetic Programming: Empirical Study [Extended Version]
Jan Žegklitz, Petr Pošík
Genetic Programming has been very successful in solving a large area of problems but its use as a machine learning algorithm has been limited so far. One of the reasons is the prob…