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        "author": "deap Development Team",
        "author_email": "deap-users@googlegroups.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
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            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "DEAP\n====\n\n|Build status| |Download| |Join the chat at https://gitter.im/DEAP/deap|\n\nDEAP is a novel evolutionary computation framework for rapid prototyping\nand testing of ideas. It seeks to make algorithms explicit and data\nstructures transparent. It works in perfect harmony with parallelisation\nmechanisms such as multiprocessing and\n`SCOOP <https://github.com/soravux/scoop>`__.\n\nDEAP includes the following features:\n\n-  Genetic algorithm using any imaginable representation\n\n   -  List, Array, Set, Dictionary, Tree, Numpy Array, etc.\n\n-  Genetic programing using prefix trees\n\n   -  Loosely typed, Strongly typed\n   -  Automatically defined functions\n\n-  Evolution strategies (including CMA-ES)\n-  Multi-objective optimisation (NSGA-II, SPEA2, MO-CMA-ES)\n-  Co-evolution (cooperative and competitive) of multiple populations\n-  Parallelization of the evaluations (and more)\n-  Hall of Fame of the best individuals that lived in the population\n-  Checkpoints that take snapshots of a system regularly\n-  Benchmarks module containing most common test functions\n-  Genealogy of an evolution (that is compatible with\n   `NetworkX <https://github.com/networkx/networkx>`__)\n-  Examples of alternative algorithms : Particle Swarm Optimization,\n   Differential Evolution, Estimation of Distribution Algorithm\n\nDownloads\n---------\n\nFollowing acceptation of `PEP\n438 <http://www.python.org/dev/peps/pep-0438/>`__ by the Python\ncommunity, we have moved DEAP\u2019s source releases on\n`PyPI <https://pypi.python.org>`__.\n\nYou can find the most recent releases at:\nhttps://pypi.python.org/pypi/deap/.\n\nDocumentation\n-------------\n\nSee the `DEAP User\u2019s Guide <http://deap.readthedocs.org/>`__ for DEAP\ndocumentation.\n\nIn order to get the tip documentation, change directory to the ``doc``\nsubfolder and type in ``make html``, the documentation will be under\n``_build/html``. You will need `Sphinx <http://sphinx.pocoo.org>`__ to\nbuild the documentation.\n\nNotebooks\n~~~~~~~~~\n\nAlso checkout our new `notebook\nexamples <https://github.com/DEAP/notebooks>`__. Using `Jupyter\nnotebooks <http://jupyter.org>`__ you\u2019ll be able to navigate and execute\neach block of code individually and tell what every line is doing.\nEither, look at the notebooks online using the notebook viewer links at\nthe botom of the page or download the notebooks, navigate to the you\ndownload directory and run\n\n.. code:: bash\n\n   jupyter notebook\n\nInstallation\n------------\n\nWe encourage you to use easy_install or pip to install DEAP on your\nsystem. Other installation procedure like apt-get, yum, etc. usually\nprovide an outdated version.\n\n.. code:: bash\n\n   pip install deap\n\nThe latest version can be installed with\n\n.. code:: bash\n\n   pip install git+https://github.com/DEAP/deap@master\n\nIf you wish to build from sources, download or clone the repository and\ntype\n\n.. code:: bash\n\n   python setup.py install\n\nBuild Status\n------------\n\nDEAP build status is available on Travis-CI\nhttps://travis-ci.org/DEAP/deap.\n\nRequirements\n------------\n\nThe most basic features of DEAP requires Python2.6. In order to combine\nthe toolbox and the multiprocessing module Python2.7 is needed for its\nsupport to pickle partial functions. CMA-ES requires Numpy, and we\nrecommend matplotlib for visualization of results as it is fully\ncompatible with DEAP\u2019s API.\n\nSince version 0.8, DEAP is compatible out of the box with Python 3. The\ninstallation procedure automatically translates the source to Python 3\nwith 2to3.\n\nExample\n-------\n\nThe following code gives a quick overview how simple it is to implement\nthe Onemax problem optimization with genetic algorithm using DEAP. More\nexamples are provided\n`here <http://deap.readthedocs.org/en/master/examples/index.html>`__.\n\n.. code:: python\n\n   import random\n   from deap import creator, base, tools, algorithms\n\n   creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))\n   creator.create(\"Individual\", list, fitness=creator.FitnessMax)\n\n   toolbox = base.Toolbox()\n\n   toolbox.register(\"attr_bool\", random.randint, 0, 1)\n   toolbox.register(\"individual\", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=100)\n   toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n\n   def evalOneMax(individual):\n       return sum(individual),\n\n   toolbox.register(\"evaluate\", evalOneMax)\n   toolbox.register(\"mate\", tools.cxTwoPoint)\n   toolbox.register(\"mutate\", tools.mutFlipBit, indpb=0.05)\n   toolbox.register(\"select\", tools.selTournament, tournsize=3)\n\n   population = toolbox.population(n=300)\n\n   NGEN=40\n   for gen in range(NGEN):\n       offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)\n       fits = toolbox.map(toolbox.evaluate, offspring)\n       for fit, ind in zip(fits, offspring):\n           ind.fitness.values = fit\n       population = toolbox.select(offspring, k=len(population))\n   top10 = tools.selBest(population, k=10)\n\nHow to cite DEAP\n----------------\n\nAuthors of scientific papers including results generated using DEAP are\nencouraged to cite the following paper.\n\n.. code:: xml\n\n   @article{DEAP_JMLR2012, \n       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 \",\n       title     = { {DEAP}: Evolutionary Algorithms Made Easy },\n       pages    = { 2171--2175 },\n       volume    = { 13 },\n       month     = { jul },\n       year      = { 2012 },\n       journal   = { Journal of Machine Learning Research }\n   }\n\nPublications on DEAP\n--------------------\n\n-  Fran\u00e7ois-Michel De Rainville, F\u00e9lix-Antoine Fortin, Marc-Andr\u00e9\n   Gardner, Marc Parizeau and Christian Gagn\u00e9, \u201cDEAP \u2013 Enabling Nimbler\n   Evolutions\u201d, SIGEVOlution, vol.\u00a06, no 2, pp.\u00a017-26, February 2014.\n   `Paper <http://goo.gl/tOrXTp>`__\n-  F\u00e9lix-Antoine Fortin, Fran\u00e7ois-Michel De Rainville, Marc-Andr\u00e9\n   Gardner, Marc Parizeau and Christian Gagn\u00e9, \u201cDEAP: Evolutionary\n   Algorithms Made Easy\u201d, Journal of Machine Learning Research, vol.\u00a013,\n   pp.\u00a02171-2175, jul 2012. `Paper <http://goo.gl/amJ3x>`__\n-  Fran\u00e7ois-Michel De Rainville, F\u00e9lix-Antoine Fortin, Marc-Andr\u00e9\n   Gardner, Marc Parizeau and Christian Gagn\u00e9, \u201cDEAP: A Python Framework\n   for Evolutionary Algorithms\u201d, in !EvoSoft Workshop, Companion proc.\n   of the Genetic and Evolutionary Computation Conference (GECCO 2012),\n   July 07-11 2012. `Paper <http://goo.gl/pXXug>`__\n\nProjects using DEAP\n-------------------\n\n-  Ribaric, T., & Houghten, S. (2017, June). Genetic programming for\n   improved cryptanalysis of elliptic curve cryptosystems. In 2017 IEEE\n   Congress on Evolutionary Computation (CEC) (pp.\u00a0419-426). IEEE.\n-  Ellefsen, Kai Olav, Herman Augusto Lepikson, and Jan C. Albiez.\n   \u201cMultiobjective coverage path planning: Enabling automated inspection\n   of complex, real-world structures.\u201d Applied Soft Computing 61 (2017):\n   264-282.\n-  S. Chardon, B. Brangeon, E. Bozonnet, C. Inard (2016), Construction\n   cost and energy performance of single family houses : From integrated\n   design to automated optimization, Automation in Construction, Volume\n   70, p.1-13.\n-  B. Brangeon, E. Bozonnet, C. Inard (2016), Integrated refurbishment\n   of collective housing and optimization process with real products\n   databases, Building Simulation Optimization, pp.\u00a0531\u2013538 Newcastle,\n   England.\n-  Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A.\n   Lavender, La Creis Kidd, and Jason H. Moore (2016). Automating\n   biomedical data science through tree-based pipeline optimization.\n   Applications of Evolutionary Computation, pages 123-137.\n-  Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H.\n   Moore (2016). Evaluation of a Tree-based Pipeline Optimization Tool\n   for Automating Data Science. Proceedings of GECCO 2016, pages\n   485-492.\n-  Van Geit W, Gevaert M, Chindemi G, R\u00f6ssert C, Courcol J, Muller EB,\n   Sch\u00fcrmann F, Segev I and Markram H (2016). BluePyOpt: Leveraging open\n   source software and cloud infrastructure to optimise model parameters\n   in neuroscience. Front. Neuroinform. 10:17. doi:\n   10.3389/fninf.2016.00017 https://github.com/BlueBrain/BluePyOpt\n-  Lara-Cabrera, R., Cotta, C. and Fern\u00e1ndez-Leiva, A.J. (2014).\n   Geometrical vs topological measures for the evolution of aesthetic\n   maps in a rts game, Entertainment Computing,\n-  Macret, M. and Pasquier, P. (2013). Automatic Tuning of the OP-1\n   Synthesizer Using a Multi-objective Genetic Algorithm. In Proceedings\n   of the 10th Sound and Music Computing Conference (SMC). (pp 614-621).\n-  Fortin, F. A., Grenier, S., & Parizeau, M. (2013, July). Generalizing\n   the improved run-time complexity algorithm for non-dominated sorting.\n   In Proceeding of the fifteenth annual conference on Genetic and\n   evolutionary computation conference (pp.\u00a0615-622). ACM.\n-  Fortin, F. A., & Parizeau, M. (2013, July). Revisiting the NSGA-II\n   crowding-distance computation. In Proceeding of the fifteenth annual\n   conference on Genetic and evolutionary computation conference\n   (pp.\u00a0623-630). ACM.\n-  Marc-Andr\u00e9 Gardner, Christian Gagn\u00e9, and Marc Parizeau. Estimation of\n   Distribution Algorithm based on Hidden Markov Models for\n   Combinatorial Optimization. in Comp. Proc. Genetic and Evolutionary\n   Computation Conference (GECCO 2013), July 2013.\n-  J. T. Zhai, M. A. Bamakhrama, and T. Stefanov. \u201cExploiting\n   Just-enough Parallelism when Mapping Streaming Applications in Hard\n   Real-time Systems\u201d. Design Automation Conference (DAC 2013), 2013.\n-  V. Akbarzadeh, C. Gagn\u00e9, M. Parizeau, M. Argany, M. A Mostafavi,\n   \u201cProbabilistic Sensing Model for Sensor Placement Optimization Based\n   on Line-of-Sight Coverage\u201d, Accepted in IEEE Transactions on\n   Instrumentation and Measurement, 2012.\n-  M. Reif, F. Shafait, and A. Dengel. \u201cDataset Generation for\n   Meta-Learning\u201d. Proceedings of the German Conference on Artificial\n   Intelligence (KI\u201912). 2012.\n-  M. T. Ribeiro, A. Lacerda, A. Veloso, and N. Ziviani.\n   \u201cPareto-Efficient Hybridization for Multi-Objective Recommender\n   Systems\u201d. Proceedings of the Conference on Recommanders Systems\n   (!RecSys\u201912). 2012.\n-  M. P\u00e9rez-Ortiz, A. Arauzo-Azofra, C. Herv\u00e1s-Mart\u00ednez, L.\n   Garc\u00eda-Hern\u00e1ndez and L. Salas-Morera. \u201cA system learning user\n   preferences for multiobjective optimization of facility layouts\u201d.\n   Pr,oceedings on the Int. Conference on Soft Computing Models in\n   Industrial and Environmental Applications (SOCO\u201912). 2012.\n-  L\u00e9vesque, J.C., Durand, A., Gagn\u00e9, C., and Sabourin, R.,\n   Multi-Objective Evolutionary Optimization for Generating Ensembles of\n   Classifiers in the ROC Space, Genetic and Evolutionary Computation\n   Conference (GECCO 2012), 2012.\n-  Marc-Andr\u00e9 Gardner, Christian Gagn\u00e9, and Marc Parizeau, \u201cBloat\n   Control in Genetic Programming with Histogram-based Accept-Reject\n   Method\u201d, in Proc. Genetic and Evolutionary Computation Conference\n   (GECCO 2011), 2011.\n-  Vahab Akbarzadeh, Albert Ko, Christian Gagn\u00e9, and Marc Parizeau,\n   \u201cTopography-Aware Sensor Deployment Optimization with CMA-ES\u201d, in\n   Proc. of Parallel Problem Solving from Nature (PPSN 2010), Springer,\n   2010.\n-  DEAP is used in `TPOT <https://github.com/rhiever/tpot>`__, an open\n   source tool that uses genetic programming to optimize machine\n   learning pipelines.\n-  DEAP is also used in ROS as an optimization package\n   http://www.ros.org/wiki/deap.\n-  DEAP is an optional dependency for\n   `PyXRD <https://github.com/mathijs-dumon/PyXRD>`__, a Python\n   implementation of the matrix algorithm developed for the X-ray\n   diffraction analysis of disordered lamellar structures.\n-  DEAP is used in `glyph <https://github.com/Ambrosys/glyph>`__, a\n   library for symbolic regression with applications to\n   `MLC <https://en.wikipedia.org/wiki/Machine_learning_control>`__.\n\nIf you want your project listed here, send us a link and a brief\ndescription and we\u2019ll be glad to add it.\n\n.. |Build status| image:: https://travis-ci.org/DEAP/deap.svg?branch=master\n   :target: https://travis-ci.org/DEAP/deap\n.. |Download| image:: https://img.shields.io/pypi/dm/deap.svg\n   :target: https://pypi.python.org/pypi/deap\n.. |Join the chat at https://gitter.im/DEAP/deap| image:: https://badges.gitter.im/Join%20Chat.svg\n   :target: https://gitter.im/DEAP/deap?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge\n\n\n",
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