Skip to main content

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/

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for evoalgos, version 1.0
Filename, size File type Python version Upload date Hashes
Filename, size evoalgos-1.0.tar.gz (41.7 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page