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Found 30 papers, 2 papers with code
The Inefficiency of Genetic Programming for Symbolic Regression -- Extended Version
This enables us to quantify the success probability of finding the best possible expressions, and to compare the search efficiency of genetic programming to random search in the space of semantically unique expressions.
A precise symbolic emulator of the linear matter power spectrum
We obtain an analytic approximation to the linear power spectrum with a root mean squared fractional error of 0. 2% between $k = 9\times10^{-3} - 9 \, h{\rm \, Mpc^{-1}}$ and across a wide range of cosmological parameters, and we provide physical interpretations for various terms in the expression.
Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction
Prediction models are trained for each cluster for the two-hour windows after meals, allowing predictions in 15-minute steps, yielding up to eight predictions at different time horizons.
Steel Phase Kinetics Modeling using Symbolic Regression
We describe an approach for empirical modeling of steel phase kinetics based on symbolic regression and genetic programming.
Identifying Differential Equations to predict Blood Glucose using Sparse Identification of Nonlinear Systems
Describing dynamic medical systems using machine learning is a challenging topic with a wide range of applications.
Comparing Shape-Constrained Regression Algorithms for Data Validation
Therefore, we require automated data validation approaches that are able to consider the prior knowledge of domain experts to produce dependable, trustworthy assessments of data quality.
Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization
Fast Function Extraction (FFX) is a deterministic algorithm for solving symbolic regression problems.
Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles
Symbolic regression is a nonlinear regression method which is commonly performed by an evolutionary computation method such as genetic programming.
Local Optimization Often is Ill-conditioned in Genetic Programming for Symbolic Regression
Gradient-based local optimization has been shown to improve results of genetic programming (GP) for symbolic regression.
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.
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.
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.
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.
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.
Data Aggregation for Reducing Training Data in Symbolic Regression
The performance of genetic programming is compared with random forests and linear regression.
Concept Drift Detection with Variable Interaction Networks
The current development of today's production industry towards seamless sensor-based monitoring is paving the way for concepts such as Predictive Maintenance.
Smooth Symbolic Regression: Transformation of Symbolic Regression into a Real-valued Optimization Problem
The typical methods for symbolic regression produce rather abrupt changes in solution candidates.
Extending a Physics-Based Constitutive Model using Genetic Programming
A major drawback is the calibration of model parameters that depend on processing conditions.
Concept for a Technical Infrastructure for Management of Predictive Models in Industrial Applications
With the increasing number of created and deployed prediction models and the complexity of machine learning workflows we require so called model management systems to support data scientists in their tasks.
Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression
We introduce in this paper a runtime-efficient tree hashing algorithm for the identification of isomorphic subtrees, with two important applications in genetic programming for symbolic regression: fast, online calculation of population diversity and algebraic simplification of symbolic expression trees.
Using Shape Constraints for Improving Symbolic Regression Models
We describe and analyze algorithms for shape-constrained symbolic regression, which allows the inclusion of prior knowledge about the shape of the regression function.
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.
On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection
Genetic programming is a powerful heuristic search technique that is used for a number of real world applications to solve among others regression, classification, and time-series forecasting problems.
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.
Declarative Modeling and Bayesian Inference of Dark Matter Halos
Probabilistic programming allows specification of probabilistic models in a declarative manner.
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.
