Metadata-Version: 1.1
Name: evoalgos
Version: 1.1
Summary: Modular evolutionary algorithms
Home-page: https://ls11-www.cs.tu-dortmund.de/people/swessing/evoalgos/doc/
Author: Simon Wessing
Author-email: simon.wessing@tu-dortmund.de
License: BSD
Description: Description
        ===========
        
        This package contains the S-metric selection evolutionary multi-objective
        optimization algorithm (SMS-EMOA) and the non-dominated sorting genetic
        algorithm 2 (NSGA2) for multiobjective optimization. For single-objective
        optimization, classical evolution strategies and the rather unknown CMSA-ES
        (covariance matrix self-adaptation evolution strategy) are provided.
        Variation for real-valued and binary search spaces is included and new
        variation operators can be easily added thanks to the modular concept.
        
        The package is geared to work with optimization problems as defined in the
        package optproblems. The whole package assumes minimization problems
        throughout!
        
        Documentation
        =============
        
        The documentation is located at
        https://www.simonwessing.de/evoalgos/doc/
        
Keywords: evolutionary optimization algorithm multiobjective EMOA MOEA SMS SBX NSGA2 hypervolume covariance self-adaptive evolution strategy nearest-better
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Requires: optproblems
Requires: numpy
