Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (
Help us improve Python packaging - Donate today!

A framework for creating evolutionary computations in Python.

Project Description

ECsPy (Evolutionary Computations in Python) is a free, open source framework for creating evolutionary computations in Python. Additionally, ECsPy provides an easy-to-use canonical genetic algorithm (GA), evolution strategy (ES), estimation of distribution algorithm (EDA), differential evolution algorithm (DEA), and particle swarm optimizer (PSO) for users who don’t need much customization.


  • Requires at least Python 2.6 (not compatible with Python 3+).
  • Numpy and Matplotlib are required if the line plot observer is used.
  • Parallel Python (pp) is required if parallel_evaluation_pp is used.


This package is distributed under the GNU General Public License version 3.0 (GPLv3). This license can be found online at

Package Structure

ECsPy consists of the following modules:

  • – provides tools for analyzing the results of an EC
  • – defines useful archiving methods, particularly for EMO algorithms
  • – defines several single- and multi-objective benchmark optimization problems
  • – provides the basic framework for an EvolutionaryComputation and specific ECs
  • – provides the Pareto class for multiobjective optimization along with specific EMOs (e.g. NSGA-II)
  • – defines useful evaluation schemes, such as parallel evaluation
  • – defines a few built-in migrators, including migration via network and migration among concurrent processes
  • – defines a few built-in observers, including screen, file, and plotting observers
  • – defines standard replacement schemes such as generational and steady-state replacement
  • – defines standard selectors (e.g., tournament)
  • – provides a basic particle swarm optimizer
  • – defines standard terminators (e.g., exceeding a maximum number of generations)
  • – defines standard topologies for particle swarms
  • – defines standard variators (crossover and mutation schemes such as n-point crossover)

Release History

This version
History Node


History Node


History Node


History Node


Download Files

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

Filename, Size & Hash SHA256 Hash Help File Type Python Version Upload Date
(173.0 kB) Copy SHA256 Hash SHA256
Source None Feb 5, 2012

Supported By

Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Google Google Cloud Servers