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…iption Change long description in setup.py
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HAL-CGP | ||
======= | ||
[](https://www.python.org/downloads/release/python-369/) | ||
[](https://www.python.org/) | ||
[](https://www.python.org/) | ||
[](https://www.gnu.org/licenses/old-licenses/gpl-3.0.html) | ||
[](https://travis-ci.org/Happy-Algorithms-League/hal-cgp) | ||
[](http://mypy-lang.org/) | ||
[](https://github.com/psf/black) | ||
[](https://coveralls.io/github/Happy-Algorithms-League/python-gp?branch=master) | ||
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Cartesian genetic programming (CGP) in pure Python. | ||
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This library implements Cartesian genetic programming (e.g, Miller and Thomson, 2000; Miller, 2011) for symbolic regression in pure Python, targeting applications with expensive fitness evaluations. It provides Python data structures to represent and evolve two-dimensional directed graphs (genotype) that are translated into computational graphs (phenotype) implementing mathematical expressions. The computational graphs can be compiled as a Python functions, SymPy expressions (Meurer et al., 2017) or PyTorch modules (Paszke et al., 2017). The library currently implements an evolutionary algorithm, specifically (mu + lambda) evolution strategies adapted from Deb et al. (2002), to evolve a population of symbolic expressions in order to optimize an objective function. |
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@@ -34,7 +34,6 @@ def read_extra_requirements(): | |
name="hal-cgp", | ||
version=read_version(), | ||
author="Jakob Jordan, Maximilian Schmidt", | ||
author_email="[email protected]", | ||
description=("Cartesian Genetic Programming in pure Python."), | ||
license="GPLv3", | ||
keywords="genetic programming", | ||
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@@ -43,7 +42,7 @@ def read_extra_requirements(): | |
install_requires=read_requirements(), | ||
extras_require=read_extra_requirements(), | ||
packages=["cgp", "cgp.ea", "cgp.local_search"], | ||
long_description=open("README.md").read(), | ||
long_description=open("long_description.md").read(), | ||
long_description_content_type="text/markdown", | ||
classifiers=[ | ||
"Development Status :: 4 - Beta", | ||
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