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Found 20 papers, 2 papers with code
Vectorial Genetic Programming -- Optimizing Segments for Feature Extraction
The presented results indicate, that the different random sampling strategies do not impact the overall algorithm performance significantly, and that the guided strategies suffer from becoming stuck in local optima.
Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization
Fast Function Extraction (FFX) is a deterministic algorithm for solving symbolic regression problems.
Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data
This contribution discusses the role of symbolic regression in Materials Science (MS) and offers a comprehensive overview of current methodological challenges and state-of-the-art results.
Cluster Analysis of a Symbolic Regression Search Space
In this chapter we take a closer look at the distribution of symbolic regression models generated by genetic programming in the search space.
Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication
Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available.
Understanding and Preparing Data of Industrial Processes for Machine Learning Applications
In cases of large proportions of missing data, imputing is often not feasible, and removing observations containing missing values is often the only option.
Preprocessing and Modeling of Radial Fan Data for Health State Prediction
The gathered data was used for modeling (and therefore monitoring) a healthy state.
Optimization Networks for Integrated Machine Learning
In this contribution we revisit the core principles of optimization networks and demonstrate their suitability for solving machine learning problems.
Complexity Measures for Multi-objective Symbolic Regression
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 a-priori.
On the Effectiveness of Genetic Operations in Symbolic Regression
This paper describes a methodology for analyzing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the fitness variation from parent to offspring.
Data Aggregation for Reducing Training Data in Symbolic Regression
The performance of genetic programming is compared with random forests and linear regression.
Extending a Physics-Based Constitutive Model using Genetic Programming
A major drawback is the calibration of model parameters that depend on processing conditions.
Contemporary Symbolic Regression Methods and their Relative Performance
We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems.
Predicting Friction System Performance with Symbolic Regression and Genetic Programming with Factor Variables
We have therefore used so-called factor variables for representing nominal variables in symbolic regression models.
Identification of Dynamical Systems using Symbolic Regression
We describe a method for the identification of models for dynamical systems from observational data.
Shape-constrained Symbolic Regression -- Improving Extrapolation with Prior Knowledge
Both algorithms are able to identify models which conform to shape constraints which is not the case for the unmodified symbolic regression algorithms.
Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure
Diversity represents an important aspect of genetic programming, being directly correlated with search performance.
Data Mining using Unguided Symbolic Regression on a Blast Furnace Dataset
The approach is based on unguided symbolic regression (every variable present in the dataset is treated as the target variable in multiple regression runs) and a novel variable relevance metric for genetic programming.
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming
In the proposed approach we use a grammar for the composition of covariance functions and genetic programming to search over the space of sentences that can be derived from the grammar.
Macro-Economic Time Series Modeling and Interaction Networks
In the proposed approach multiple symbolic regression runs are executed for each variable of the dataset to find potentially interesting models.
