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
