Symbolic regression

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  • On the other hand, this work presents the first application of RANSAC to symbolic regression with GP, with impressive results.[1]
  • Discovering unmodeled components in astrodynamics with symbolic regression.[2]
  • The paper explores the use of symbolic regression to discover missing parts of the dynamics of space objects from tracking data.[2]
  • The paper presents a simple, yet representative, example of incomplete orbital dynamics to test the use of symbolic regression.[2]
  • The process of generating a computer program to fit numerical data is called symbolic regression.[3]
  • The authors showcase the potential of symbolic regression as an analytic method for use in materials research.[4]
  • Next, the authors discuss industrial applications of symbolic regression and its potential applications in materials science.[4]
  • In this prospective paper, we focus on an alternative to machine-learning models: symbolic regression.[5]
  • I think symbolic regression is a great tool to be aware of.[5]
  • By not requiring a specific model to be specified, symbolic regression isn't affected by human bias, or unknown gaps in domain knowledge.[6]
  • Symbolic regression is one of the best known problems in GP (see Reference).[7]
  • As any evolutionary program, symbolic regression needs (at least) two object types : an individual containing the genotype and a fitness.[7]
  • "Predicting friction system performance with symbolic regression and genetic programming with factor variables".[8]
  • “Prediction of Stress-Strain Curves for Aluminium Alloys using Symbolic Regression”.[8]
  • Symbolic regression is a data-based modelling method where the goal is to find a formula that describes given data.[8]
  • However, in symbolic regression one does not merely fit parameters to a fixed model structure.[8]
  • One of the more interesting ones is symbolic regression, which is used in Eureqa models within DataRobot.[9]
  • Perhaps the most common technique used in symbolic regression is genetic programming2 (GP).[9]
  • Let’s use the “Auto MPG” data from UCI: http://archive.ics.uci.edu/ml/datasets/Auto+MPG to understand symbolic regression.[9]
  • One of the key decisions in symbolic regression is how to represent the programs we are creating3.[9]
  • This was an example of symbolic regression: discovering a symbolic expression that accurately matches a given dataset.[10]
  • However, as we will see below, this does not prevent us from discovering and exploiting these properties to facilitate symbolic regression.[10]
  • Here is my code to compute Symbolic Regression and plot the function.[11]
  • Symbolic Regression also tries to fit observed experimental data.[12]
  • There are different ways to represent the solutions in Symbolic Regression.[12]
  • In Symbolic Regression, many initially random symbolic equations compete to model experimental data in the most promising way.[12]
  • Symbolic Regression is used to solve this task.[12]
  • It is known that symbolic regression is a widely used method for mathematical function approximation.[13]
  • The flowchart of symbolic regression based on genetic programming (see more details of this flowchart and SR in Supplementary Information).[14]
  • Symbolic regression (SR) is a powerful method for building predictive models from data without assuming any model structure.[15]
  • In the majority of work exploring symbolic regression, features are used directly without acknowledgement of their relative scale or unit.[16]
  • This paper extends recent work on the importance of standardisation of features when conducting symbolic regression.[16]
  • Other symbolic regression libraries Due to its popularity, symbolic regression is implemented by most genetic programming libraries.[17]
  • Right: gp-based symbolic regression finds different candidate control laws.[18]

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