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Modular evolutionary algorithms

Project 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://ls11-www.cs.tu-dortmund.de/people/swessing/evoalgos/doc/

Release history Release notifications

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1.0

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0.9

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0.1

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Filename, size & hash SHA256 hash help File type Python version Upload date
evoalgos-1.0.tar.gz (41.7 kB) Copy SHA256 hash SHA256 Source None Dec 22, 2017

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