Skip to search formSkip to main contentSkip to account menu

Symbolic regression

Symbolic regression is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given… 
Wikipedia (opens in a new tab)

Related topics

Related topics

8 relations
EureqaEvolutionary algorithmFitness functionGenetic programming

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2015
2015
Variational Genetic Programming for Optimal Control System Synthesis of Mobile Robots
2015
2015
Energy cost forecasting for event venues
Electricity price, consumption, and demand forecasting has been a topic of research interest for a long time. The proliferation… 
2013
2013
Determining the damping behavior of fiber reinforced composites: A new approach to find mathematical relationships in data sets
2010
2010
An Efficient Selection Strategy for Digital Circuit Evolution
In this paper, we propose a new modification of Cartesian Genetic Programming (CGP) that enables to optimize digital circuits… 
2009
2009
Profiling Symbolic Regression-Classification
This chapter performance-profiles a composite method of symbolic regression-classification, combining standard genetic programming… 
2008
2008
Parallel hybrid evolutionary computation: Automatic tuning of parameters for parallel gene expression programming
2007
2007
Predicting Prime Numbers Using Cartesian Genetic Programming
Prime generating polynomial functions are known that can produce sequences of prime numbers (e.g. Euler polynomials). However… 
2001
2001
An Adaptive Mapping for Developmental Genetic Programming
In this article we introduce a general framework for constructing an adaptive genotype-to-phenotype mapping, and apply it to… 
1998
1998
Optical scatterometry of quarter-micron patterns using neural regression
With shrinking dimensions and increasing chip areas, a rapid and non-destructive full wafer characterization after every… 
1987
1987
Directional Tracking of Regressiontype Model Parameters
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)