# Symbolic regression

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## 노트

### 위키데이터

- ID : Q831366

### 말뭉치

- 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]}

### 소스

- ↑ RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming
- ↑
^{2.0}^{2.1}^{2.2}Discovering unmodeled components in astrodynamics with symbolic regression - ↑ Symbolic Regression Genetic Programming Example
- ↑
^{4.0}^{4.1}Symbolic regression in materials science - ↑
^{5.0}^{5.1}gplearn Symbolic Regression - ↑ Symbolic regression
- ↑
^{7.0}^{7.1}Symbolic Regression Problem: Introduction to GP — DEAP 1.3.1 documentation - ↑
^{8.0}^{8.1}^{8.2}^{8.3}Josef Ressel Centre for Symbolic Regression - ↑
^{9.0}^{9.1}^{9.2}^{9.3}Symbolic Regression from Scratch with Python - ↑
^{10.0}^{10.1}AI Feynman: A physics-inspired method for symbolic regression - ↑ How to get the function result from Symbolic Regression with R
- ↑
^{12.0}^{12.1}^{12.2}^{12.3}Learn More about Your Data: A Symbolic Regression Knowledge Representation Framework - ↑ Symbolic Regression Problems by Genetic Programming with Multi-branches
- ↑ Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
- ↑ Benchmarking state-of-the-art symbolic regression algorithms
- ↑
^{16.0}^{16.1}Feature standardisation and coefficient optimisation for effective symbolic regression - ↑ Glyph: Symbolic Regression Tools
- ↑ (PDF) Glyph: Symbolic Regression Tools

## 메타데이터

### 위키데이터

- ID : Q831366