9 citations · 21 across the 4 of their papers we have counts for
4 papers
Symbolic Regression Methods for Reinforcement Learning
Jiří Kubalík, Erik Derner, Jan Žegklitz +1
Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithm…
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…
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…
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…