Metadata-Version: 1.1
Name: deap
Version: 1.3.0
Summary: Distributed Evolutionary Algorithms in Python
Home-page: https://www.github.com/deap
Author: deap Development Team
Author-email: deap-users@googlegroups.com
License: LGPL
Description: DEAP
        ====
        
        |Build status| |Download| |Join the chat at https://gitter.im/DEAP/deap|
        
        DEAP is a novel evolutionary computation framework for rapid prototyping
        and testing of ideas. It seeks to make algorithms explicit and data
        structures transparent. It works in perfect harmony with parallelisation
        mechanisms such as multiprocessing and
        `SCOOP <https://github.com/soravux/scoop>`__.
        
        DEAP includes the following features:
        
        -  Genetic algorithm using any imaginable representation
        
           -  List, Array, Set, Dictionary, Tree, Numpy Array, etc.
        
        -  Genetic programing using prefix trees
        
           -  Loosely typed, Strongly typed
           -  Automatically defined functions
        
        -  Evolution strategies (including CMA-ES)
        -  Multi-objective optimisation (NSGA-II, SPEA2, MO-CMA-ES)
        -  Co-evolution (cooperative and competitive) of multiple populations
        -  Parallelization of the evaluations (and more)
        -  Hall of Fame of the best individuals that lived in the population
        -  Checkpoints that take snapshots of a system regularly
        -  Benchmarks module containing most common test functions
        -  Genealogy of an evolution (that is compatible with
           `NetworkX <https://github.com/networkx/networkx>`__)
        -  Examples of alternative algorithms : Particle Swarm Optimization,
           Differential Evolution, Estimation of Distribution Algorithm
        
        Downloads
        ---------
        
        Following acceptation of `PEP
        438 <http://www.python.org/dev/peps/pep-0438/>`__ by the Python
        community, we have moved DEAP’s source releases on
        `PyPI <https://pypi.python.org>`__.
        
        You can find the most recent releases at:
        https://pypi.python.org/pypi/deap/.
        
        Documentation
        -------------
        
        See the `DEAP User’s Guide <http://deap.readthedocs.org/>`__ for DEAP
        documentation.
        
        In order to get the tip documentation, change directory to the ``doc``
        subfolder and type in ``make html``, the documentation will be under
        ``_build/html``. You will need `Sphinx <http://sphinx.pocoo.org>`__ to
        build the documentation.
        
        Notebooks
        ~~~~~~~~~
        
        Also checkout our new `notebook
        examples <https://github.com/DEAP/notebooks>`__. Using `Jupyter
        notebooks <http://jupyter.org>`__ you’ll be able to navigate and execute
        each block of code individually and tell what every line is doing.
        Either, look at the notebooks online using the notebook viewer links at
        the botom of the page or download the notebooks, navigate to the you
        download directory and run
        
        .. code:: bash
        
           jupyter notebook
        
        Installation
        ------------
        
        We encourage you to use easy_install or pip to install DEAP on your
        system. Other installation procedure like apt-get, yum, etc. usually
        provide an outdated version.
        
        .. code:: bash
        
           pip install deap
        
        The latest version can be installed with
        
        .. code:: bash
        
           pip install git+https://github.com/DEAP/deap@master
        
        If you wish to build from sources, download or clone the repository and
        type
        
        .. code:: bash
        
           python setup.py install
        
        Build Status
        ------------
        
        DEAP build status is available on Travis-CI
        https://travis-ci.org/DEAP/deap.
        
        Requirements
        ------------
        
        The most basic features of DEAP requires Python2.6. In order to combine
        the toolbox and the multiprocessing module Python2.7 is needed for its
        support to pickle partial functions. CMA-ES requires Numpy, and we
        recommend matplotlib for visualization of results as it is fully
        compatible with DEAP’s API.
        
        Since version 0.8, DEAP is compatible out of the box with Python 3. The
        installation procedure automatically translates the source to Python 3
        with 2to3.
        
        Example
        -------
        
        The following code gives a quick overview how simple it is to implement
        the Onemax problem optimization with genetic algorithm using DEAP. More
        examples are provided
        `here <http://deap.readthedocs.org/en/master/examples/index.html>`__.
        
        .. code:: python
        
           import random
           from deap import creator, base, tools, algorithms
        
           creator.create("FitnessMax", base.Fitness, weights=(1.0,))
           creator.create("Individual", list, fitness=creator.FitnessMax)
        
           toolbox = base.Toolbox()
        
           toolbox.register("attr_bool", random.randint, 0, 1)
           toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=100)
           toolbox.register("population", tools.initRepeat, list, toolbox.individual)
        
           def evalOneMax(individual):
               return sum(individual),
        
           toolbox.register("evaluate", evalOneMax)
           toolbox.register("mate", tools.cxTwoPoint)
           toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
           toolbox.register("select", tools.selTournament, tournsize=3)
        
           population = toolbox.population(n=300)
        
           NGEN=40
           for gen in range(NGEN):
               offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)
               fits = toolbox.map(toolbox.evaluate, offspring)
               for fit, ind in zip(fits, offspring):
                   ind.fitness.values = fit
               population = toolbox.select(offspring, k=len(population))
           top10 = tools.selBest(population, k=10)
        
        How to cite DEAP
        ----------------
        
        Authors of scientific papers including results generated using DEAP are
        encouraged to cite the following paper.
        
        .. code:: xml
        
           @article{DEAP_JMLR2012, 
               author    = " F\'elix-Antoine Fortin and Fran\c{c}ois-Michel {De Rainville} and Marc-Andr\'e Gardner and Marc Parizeau and Christian Gagn\'e ",
               title     = { {DEAP}: Evolutionary Algorithms Made Easy },
               pages    = { 2171--2175 },
               volume    = { 13 },
               month     = { jul },
               year      = { 2012 },
               journal   = { Journal of Machine Learning Research }
           }
        
        Publications on DEAP
        --------------------
        
        -  François-Michel De Rainville, Félix-Antoine Fortin, Marc-André
           Gardner, Marc Parizeau and Christian Gagné, “DEAP – Enabling Nimbler
           Evolutions”, SIGEVOlution, vol. 6, no 2, pp. 17-26, February 2014.
           `Paper <http://goo.gl/tOrXTp>`__
        -  Félix-Antoine Fortin, François-Michel De Rainville, Marc-André
           Gardner, Marc Parizeau and Christian Gagné, “DEAP: Evolutionary
           Algorithms Made Easy”, Journal of Machine Learning Research, vol. 13,
           pp. 2171-2175, jul 2012. `Paper <http://goo.gl/amJ3x>`__
        -  François-Michel De Rainville, Félix-Antoine Fortin, Marc-André
           Gardner, Marc Parizeau and Christian Gagné, “DEAP: A Python Framework
           for Evolutionary Algorithms”, in !EvoSoft Workshop, Companion proc.
           of the Genetic and Evolutionary Computation Conference (GECCO 2012),
           July 07-11 2012. `Paper <http://goo.gl/pXXug>`__
        
        Projects using DEAP
        -------------------
        
        -  Ribaric, T., & Houghten, S. (2017, June). Genetic programming for
           improved cryptanalysis of elliptic curve cryptosystems. In 2017 IEEE
           Congress on Evolutionary Computation (CEC) (pp. 419-426). IEEE.
        -  Ellefsen, Kai Olav, Herman Augusto Lepikson, and Jan C. Albiez.
           “Multiobjective coverage path planning: Enabling automated inspection
           of complex, real-world structures.” Applied Soft Computing 61 (2017):
           264-282.
        -  S. Chardon, B. Brangeon, E. Bozonnet, C. Inard (2016), Construction
           cost and energy performance of single family houses : From integrated
           design to automated optimization, Automation in Construction, Volume
           70, p.1-13.
        -  B. Brangeon, E. Bozonnet, C. Inard (2016), Integrated refurbishment
           of collective housing and optimization process with real products
           databases, Building Simulation Optimization, pp. 531–538 Newcastle,
           England.
        -  Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A.
           Lavender, La Creis Kidd, and Jason H. Moore (2016). Automating
           biomedical data science through tree-based pipeline optimization.
           Applications of Evolutionary Computation, pages 123-137.
        -  Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H.
           Moore (2016). Evaluation of a Tree-based Pipeline Optimization Tool
           for Automating Data Science. Proceedings of GECCO 2016, pages
           485-492.
        -  Van Geit W, Gevaert M, Chindemi G, Rössert C, Courcol J, Muller EB,
           Schürmann F, Segev I and Markram H (2016). BluePyOpt: Leveraging open
           source software and cloud infrastructure to optimise model parameters
           in neuroscience. Front. Neuroinform. 10:17. doi:
           10.3389/fninf.2016.00017 https://github.com/BlueBrain/BluePyOpt
        -  Lara-Cabrera, R., Cotta, C. and Fernández-Leiva, A.J. (2014).
           Geometrical vs topological measures for the evolution of aesthetic
           maps in a rts game, Entertainment Computing,
        -  Macret, M. and Pasquier, P. (2013). Automatic Tuning of the OP-1
           Synthesizer Using a Multi-objective Genetic Algorithm. In Proceedings
           of the 10th Sound and Music Computing Conference (SMC). (pp 614-621).
        -  Fortin, F. A., Grenier, S., & Parizeau, M. (2013, July). Generalizing
           the improved run-time complexity algorithm for non-dominated sorting.
           In Proceeding of the fifteenth annual conference on Genetic and
           evolutionary computation conference (pp. 615-622). ACM.
        -  Fortin, F. A., & Parizeau, M. (2013, July). Revisiting the NSGA-II
           crowding-distance computation. In Proceeding of the fifteenth annual
           conference on Genetic and evolutionary computation conference
           (pp. 623-630). ACM.
        -  Marc-André Gardner, Christian Gagné, and Marc Parizeau. Estimation of
           Distribution Algorithm based on Hidden Markov Models for
           Combinatorial Optimization. in Comp. Proc. Genetic and Evolutionary
           Computation Conference (GECCO 2013), July 2013.
        -  J. T. Zhai, M. A. Bamakhrama, and T. Stefanov. “Exploiting
           Just-enough Parallelism when Mapping Streaming Applications in Hard
           Real-time Systems”. Design Automation Conference (DAC 2013), 2013.
        -  V. Akbarzadeh, C. Gagné, M. Parizeau, M. Argany, M. A Mostafavi,
           “Probabilistic Sensing Model for Sensor Placement Optimization Based
           on Line-of-Sight Coverage”, Accepted in IEEE Transactions on
           Instrumentation and Measurement, 2012.
        -  M. Reif, F. Shafait, and A. Dengel. “Dataset Generation for
           Meta-Learning”. Proceedings of the German Conference on Artificial
           Intelligence (KI’12). 2012.
        -  M. T. Ribeiro, A. Lacerda, A. Veloso, and N. Ziviani.
           “Pareto-Efficient Hybridization for Multi-Objective Recommender
           Systems”. Proceedings of the Conference on Recommanders Systems
           (!RecSys’12). 2012.
        -  M. Pérez-Ortiz, A. Arauzo-Azofra, C. Hervás-Martínez, L.
           García-Hernández and L. Salas-Morera. “A system learning user
           preferences for multiobjective optimization of facility layouts”.
           Pr,oceedings on the Int. Conference on Soft Computing Models in
           Industrial and Environmental Applications (SOCO’12). 2012.
        -  Lévesque, J.C., Durand, A., Gagné, C., and Sabourin, R.,
           Multi-Objective Evolutionary Optimization for Generating Ensembles of
           Classifiers in the ROC Space, Genetic and Evolutionary Computation
           Conference (GECCO 2012), 2012.
        -  Marc-André Gardner, Christian Gagné, and Marc Parizeau, “Bloat
           Control in Genetic Programming with Histogram-based Accept-Reject
           Method”, in Proc. Genetic and Evolutionary Computation Conference
           (GECCO 2011), 2011.
        -  Vahab Akbarzadeh, Albert Ko, Christian Gagné, and Marc Parizeau,
           “Topography-Aware Sensor Deployment Optimization with CMA-ES”, in
           Proc. of Parallel Problem Solving from Nature (PPSN 2010), Springer,
           2010.
        -  DEAP is used in `TPOT <https://github.com/rhiever/tpot>`__, an open
           source tool that uses genetic programming to optimize machine
           learning pipelines.
        -  DEAP is also used in ROS as an optimization package
           http://www.ros.org/wiki/deap.
        -  DEAP is an optional dependency for
           `PyXRD <https://github.com/mathijs-dumon/PyXRD>`__, a Python
           implementation of the matrix algorithm developed for the X-ray
           diffraction analysis of disordered lamellar structures.
        -  DEAP is used in `glyph <https://github.com/Ambrosys/glyph>`__, a
           library for symbolic regression with applications to
           `MLC <https://en.wikipedia.org/wiki/Machine_learning_control>`__.
        
        If you want your project listed here, send us a link and a brief
        description and we’ll be glad to add it.
        
        .. |Build status| image:: https://travis-ci.org/DEAP/deap.svg?branch=master
           :target: https://travis-ci.org/DEAP/deap
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           :target: https://pypi.python.org/pypi/deap
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Keywords: evolutionary algorithms,genetic algorithms,genetic programming,cma-es,ga,gp,es,pso
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
