Welcome to Thefittest’s documentation!


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thefittest is an open-source library designed for the efficient application of classical evolutionary algorithms and their effective modifications in optimization and machine learning. Our project aims to provide performance, accessibility, and ease of use, opening up the world of advanced evolutionary methods to you.

Features of thefittest

Performance

Our library is developed using advanced coding practices and delivers high performance through integration with NumPy, Scipy, Numba, and scikit-learn.

Versatility

thefittest offers a wide range of classical evolutionary algorithms and effective modifications, making it the ideal choice for a variety of optimization and machine learning tasks.

Integration with scikit-learn

Easily integrate machine learning methods from thefittest with scikit-learn tools, creating comprehensive and versatile solutions for evolutionary optimization and model training tasks.

Installation

pip install thefittest

Dependencies

thefittest requires:

thefittest contains methods

Benchmarks

Examples

Notebooks on how to use thefittest:

If some notebooks are too big to display, you can use NBviewer.

Contents: