ecspy 1.1

A framework for creating evolutionary computations in Python.

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)

Resources

• Homepage: http://ecspy.googlecode.com

• Email: [e-mail]