# Welcome to gplearn’s documentation!

\[One \,general \,law, \,leading \,to \,the \,advancement \,of \,all \,organic \,beings, namely,\]
\[multiply, \,vary, \,let \,the \,strongest \,live \,and \,the \,weakest \,die.\]
\[- Charles \,Darwin, \,On \,the \,Origin \,of \,Species \,(1859)\]

`gplearn` implements Genetic Programming in Python, with a scikit-learn inspired and compatible API.

While Genetic Programming (GP) can be used to perform a very wide variety of tasks, `gplearn` is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straight-forward to implement.

Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations.

`gplearn` retains the familiar scikit-learn `fit`/`predict` API and works with the existing scikit-learn pipeline and grid search modules. You can get started with `gplearn` as simply as:

    est = SymbolicRegressor()
    est.fit(X_train, y_train)
    y_pred = est.predict(X_test)

However, don’t let that stop you from exploring all the ways that the evolution can be tailored to your problem. The package attempts to squeeze a lot of functionality into a scikit-learn-style API. While there are a lot of parameters to tweak, reading the documentation here should make the more relevant ones clear for your problem.

`gplearn` supports regression through the `SymbolicRegressor`, binary classification with the `SymbolicClassifier`, as well as transformation for automated feature engineering with the `SymbolicTransformer`, which is designed to support regression problems, but should also work for binary classification.

`gplearn` is built on scikit-learn and a fairly recent copy (0.22.1+) is required for installation. If you come across any issues in running or installing the package, please submit a bug report.

Next up, read some more details about what Genetic Programming is, and how it works…

Contents:

- Introduction to GP
  - Representation
  - Fitness
  - Closure
  - Sufficiency
  - Initialization
  - Selection
  - Evolution
  - Termination
  - Bloat
  - Classification
  - Transformer
- Examples
  - Symbolic Regressor
  - Symbolic Transformer
  - Symbolic Classifier
- API reference
  - Symbolic Regressor
  - Symbolic Classifier
  - Symbolic Transformer
  - User-Defined Functions
  - User-Defined Fitness Metrics
- Advanced Use
  - Introspecting Programs
  - Running Evolution in Parallel
  - Exporting
  - Custom Functions
  - Custom Fitness
  - Continuing Evolution
- Installation
- Contributing
- Release History
  - Version 0.4.2 - 3 May 2022
  - Version 0.4.1 - 1 Jun 2019
  - Version 0.4.0 - 23 Apr 2019
  - Version 0.3.0 - 23 Nov 2017
  - Version 0.2.0 - 30 Mar 2017
  - Version 0.1.0 - 6 May 2015