Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.1K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.1K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

3.2K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
3.2K
Regression Toward the Mean01:52

Regression Toward the Mean

6.6K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.6K
Regression Analysis01:11

Regression Analysis

6.5K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.5K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

707
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
707
Prediction Intervals01:03

Prediction Intervals

2.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

SRBench++ : principled benchmarking of symbolic regression with domain-expert interpretation.

IEEE transactions on evolutionary computation : a publication of the IEEE Neural Networks Council·2025
Same author

Interaction-Transformation Evolutionary Algorithm for Symbolic Regression.

Evolutionary computation·2020
Same author

Evaluation of the implementation of radiation protection measures for aircrew in EU member states.

Radiation protection dosimetry·2009
Same author

Selectivity and dynamic behavior of glass electrodes.

Advances in colloid and interface science·2005
Same author

Screening of patients with Turner syndrome for "hidden" Y-mosaicism.

Klinische Padiatrie·1999
Same author

[Long-term follow-up of traumatic hip fracture in childhood].

Helvetica chirurgica acta·1994
Same journal

Computing Optimal Populations for Binary Problems using Logic Minimization.

Evolutionary computation·2026
Same journal

Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training.

Evolutionary computation·2026
Same journal

XCS for Sequential Perceptual Aliasing in Multi-Step Decision Making.

Evolutionary computation·2026
Same journal

A dynamic multi-objective evolutionary algorithm using dual-space prediction and surrogate-based sampling.

Evolutionary computation·2026
Same journal

Adapting MOEA/D to CMA-ES for Dealing with Ill-conditioned Multiobjective Problems.

Evolutionary computation·2026
Same journal

Editorial of the Special Issue: Parallel Problem Solving from Nature PPSN 2024 Extended Versions of Best Paper Candidates.

Evolutionary computation·2026
See all related articles

Related Experiment Video

Updated: Oct 17, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.2K

Shape-Constrained Symbolic Regression-Improving Extrapolation with Prior Knowledge.

G Kronberger1, F O de Franca2, B Burlacu3

  • 1Josef Ressel Center for Symbolic Regression, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria gabriel.kronberger@fh-ooe.at.

Evolutionary Computation
|October 8, 2021
PubMed
Summary
This summary is machine-generated.

Shape-constrained symbolic regression incorporates prior knowledge to guide model behavior, improving extrapolation. While constraints ensure adherence to expected patterns, they may reduce predictive accuracy on training and test data.

Keywords:
Symbolic regressiongenetic programmingshape-constrained regression

More Related Videos

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.3K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.4K

Related Experiment Videos

Last Updated: Oct 17, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.2K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.3K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.4K

Area of Science:

  • Computational intelligence
  • Machine learning
  • Symbolic regression

Background:

  • Symbolic regression (SR) aims to discover mathematical expressions from data.
  • Incorporating prior knowledge into SR models is challenging.
  • Existing SR methods may lack robustness and extrapolation capabilities.

Purpose of the Study:

  • To introduce shape-constrained symbolic regression (SCSR) for integrating prior knowledge.
  • To enforce function image and derivative constraints, such as monotonicity.
  • To improve model conformity to expected behavior and enhance extrapolation.

Main Methods:

  • Developed two evolutionary algorithms for SCSR: modified genetic programming and a two-population algorithm.
  • Utilized interval arithmetic for approximating bounds of models and their derivatives.
  • Tested algorithms on 19 synthetic and 4 real-world regression problems.

Main Results:

  • Both SCSR algorithms successfully identified models adhering to shape constraints, unlike standard SR.
  • Models with constraints showed reduced predictive accuracy on training and test sets.
  • Shape-constrained polynomial regression yielded the best test set results, albeit with larger models.

Conclusions:

  • SCSR is feasible for enforcing prior knowledge, such as monotonicity, in symbolic regression.
  • While improving model interpretability and extrapolation potential, constraints can impact predictive accuracy.
  • Further research into balancing constraints and accuracy is warranted, with shape-constrained polynomial regression showing promise.