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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.

Requirements

  • 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.

License

This package is distributed under the GNU General Public License version 3.0 (GPLv3). This license can be found online at http://www.opensource.org/licenses/gpl-3.0.html.

Package Structure

ECsPy consists of the following modules:

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

Project details


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