from setuptools import setup

setup(
	name='sds',
	version='0.1.5',
	packages=['sds'],
	description='Stochastic Diffusion Search',
	keywords = ['swarm','artificial','intelligence','search'],
	classifiers = [
		'Development Status :: 3 - Alpha',
		'Intended Audience :: Developers',
		'Intended Audience :: Science/Research',
		'License :: OSI Approved :: Apache Software License',
		'Operating System :: OS Independent',
		'Programming Language :: Python :: 3',
		'Programming Language :: Python',
		'Topic :: Scientific/Engineering :: Artificial Intelligence',
	],
	url='http://www.aomartin.co.uk/sds-library/',
	author='Andrew Owen Martin',
	author_email='a.martin@gold.ac.uk',
	long_description="""\
A library which implements the main variants of Stochastic Diffusion
Search (SDS), and provides a convenient front end.

Stochastic Diffusion Search (SDS) is a generic population-based search
method. SDS agents perform cheap, partial evaluations of a hypothesis (a
candidate solution to the search problem). They then share information
about hypotheses (diffusion of information) through direct one-to-one
communication. As a result of the diffusion mechanism, high-quality
solutions can be identified from clusters of agents with the same
hypothesis.

This is a library used during the writing of my PhD thesis, I will
publish full documentation and host the code on GitHub once the design
has settled down and I have submitted my thesis. Until then, feel free
to email me.

SDS has a Scholarpedia page:
http://www.scholarpedia.org/article/Stochastic_diffusion_search

A list of papers written on SDS can be found in the Stochastic Diffusion
Search paper repository, maintained by the author of this module:
http://www.aomartin.co.uk/sds-repository/publications.html
"""
)
