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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… Expand
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Papers overview

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Highly Cited
2012
Highly Cited
2012
Artificial bee colony algorithm simulating the intelligent foraging behavior of honey bee swarms is one of the most popular swarm… Expand
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Highly Cited
2011
Highly Cited
2011
Symbolic regression is a common application for genetic programming (GP). This paper presents a new non-evolutionary technique… Expand
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Highly Cited
2010
Highly Cited
2010
In this contribution GPTIPS, a free, open source MATLAB toolbox for performing symbolic regression by genetic programming (GP) is… Expand
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Highly Cited
2010
Highly Cited
2010
Traditional Symbolic Regression applications are a form of supervised learning, where a label y is provided for every \(\vec{x… Expand
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Highly Cited
2009
Highly Cited
2009
This paper presents a novel approach to generate data-driven regression models that not only give reliable prediction of the… Expand
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Highly Cited
2006
Highly Cited
2006
This paper describes a new hybrid regression method that combines the best features of conventional numerical regression… Expand
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Highly Cited
2005
Highly Cited
2005
Symbolic regression via genetic programming (hereafter, referred to simply as symbolic regression) has proven to be a very… Expand
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Highly Cited
2003
Highly Cited
2003
The use of protected operators and squared error measures are standard approaches in symbolic regression. It will be shown that… Expand
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Highly Cited
2003
Highly Cited
2003
This paper describes a new method for creating polynomial regression models. The new method is compared with stepwise regression… Expand
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Highly Cited
2000
Highly Cited
2000
Presents an implementation of symbolic regression which is based on genetic programming (GP). Unfortunately, standard… Expand
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