Metadata-Version: 1.0
Name: ecspy
Version: 0.6
Summary: A framework for creating evolutionary computations in Python.
Home-page: http://ecspy.googlecode.com
Author: Aaron Garrett
Author-email: aaron.lee.garrett@gmail.com
License: GPLv3+
Download-URL: http://ecspy.googlecode.com/files/ecspy-0.6.zip
Description: ``ecspy`` -- 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.
          * Numpy is required if the screen or file observers are used.
          * Matplotlib is required if the line plot observer is used.
          * Parallel Python (pp) is required if parallel evaluation 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
          
          * 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 basic default migration which does nothing
        
          * 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: aaron.lee.garrett@gmail.com
          
        
Keywords: python,optimization,evolutionary,computation,genetic algorithm,particle swarm,estimation of distribution,differential evolution,nsga,paes
Platform: Any
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
