Metadata-Version: 1.1
Name: inspyred
Version: 1.0.1
Summary: A framework for creating bio-inspired computational intelligence algorithms in Python
Home-page: https://inspyred.github.io
Author: Aaron Garrett
Author-email: aaron.lee.garrett@gmail.com
License: MIT
Description: ``inspyred`` -- A framework for creating bio-inspired computational intelligence algorithms in Python.
        ------------------------------------------------------------------------------------------------------
        
        inspyred is a free, open source framework for creating biologically-inspired 
        computational intelligence algorithms in Python, including evolutionary 
        computation, swarm intelligence, and immunocomputing. Additionally, inspyred 
        provides easy-to-use canonical versions of many bio-inspired algorithms for 
        users who do not need much customization.
        
        
        Example
        =======
        
        The following example illustrates the basics of the inspyred package. In this 
        example, candidate solutions are 10-bit binary strings whose decimal values 
        should be maximized::
        
            import random 
            import time 
            import inspyred
            
            def generate_binary(random, args):
                bits = args.get('num_bits', 8)
                return [random.choice([0, 1]) for i in range(bits)]
            
            @inspyred.ec.evaluators.evaluator
            def evaluate_binary(candidate, args):
                return int("".join([str(c) for c in candidate]), 2)
            
            rand = random.Random()
            rand.seed(int(time.time()))
            ga = inspyred.ec.GA(rand)
            ga.observer = inspyred.ec.observers.stats_observer
            ga.terminator = inspyred.ec.terminators.evaluation_termination
            final_pop = ga.evolve(evaluator=evaluate_binary,
                                  generator=generate_binary,
                                  max_evaluations=1000,
                                  num_elites=1,
                                  pop_size=100,
                                  num_bits=10)
            final_pop.sort(reverse=True)
            for ind in final_pop:
                print(str(ind))
        
        
        Requirements
        ============
        
          * Requires at least Python 2.6+ or 3+.
          * Numpy and Pylab are required for several functions in ``ec.observers``.
          * Pylab and Matplotlib are required for several functions in ``ec.analysis``.
          * Parallel Python (pp) is required if ``ec.evaluators.parallel_evaluation_pp`` is used.
        
        
        License
        =======
        
        This package is distributed under the MIT License. This license can be found 
        online at http://www.opensource.org/licenses/MIT.
          
        
        Resources
        =========
        
          * Homepage: http://aarongarrett.github.io/inspyred
          * Email: aaron.lee.garrett@gmail.com
        
Keywords: optimization evolutionary computation genetic algorithm particle swarm estimation distribution differential evolution nsga paes island model multiobjective ant colony
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
