19 citations · 77 across the 12 of their papers we have counts for
15 papers
Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles
Fabricio Olivetti de Franca, Gabriel Kronberger
Symbolic regression is a nonlinear regression method which is commonly performed by an evolutionary computation method such as genetic programming. Quantification of uncertainty of…
Cluster Analysis of a Symbolic Regression Search Space
Gabriel Kronberger, Lukas Kammerer, Bogdan Burlacu +3
In this chapter we take a closer look at the distribution of symbolic regression models generated by genetic programming in the search space. The motivation for this work is to imp…
Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication
Lukas Kammerer, Gabriel Kronberger, Bogdan Burlacu +3
Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available. Such scenarios often require spe…
Optimization Networks for Integrated Machine Learning
Michael Kommenda, Johannes Karder, Andreas Beham +4
Optimization networks are a new methodology for holistically solving interrelated problems that have been developed with combinatorial optimization problems in mind. In this contri…
Complexity Measures for Multi-objective Symbolic Regression
Michael Kommenda, Andreas Beham, Michael Affenzeller +1
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…
Data Aggregation for Reducing Training Data in Symbolic Regression
Lukas Kammerer, Gabriel Kronberger, Michael Kommenda
The growing volume of data makes the use of computationally intense machine learning techniques such as symbolic regression with genetic programming more and more impractical. This…