Package: deap
DEAPDEAP 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 andSCOOP.DEAP includes the following features:Genetic algorithm using any imaginable representationList, Array, Set, Dictionary, Tree, Numpy Array, etc.Genetic programming using prefix treesLoosely typed, Strongly typedAutomatically defined functionsEvolution strategies (including CMA-ES)Multi-objective optimisation (NSGA-II, NSGA-III, SPEA2, MO-CMA-ES)Co-evolution (cooperative and competitive) of multiple populationsParallelization of the evaluations (and more)Hall of Fame of the best individuals that lived in the populationCheckpoints that take snapshots of a system regularlyBenchmarks module containing most common test functionsGenealogy of an evolution (that is compatible withNetworkX)Examples of alternative algorithms : Particle Swarm Optimization, Differential Evolution, Estimation of Distribution AlgorithmDownloadsFollowing acceptance ofPEP 438by the Python community, we have moved DEAP's source releases onPyPI.You can find the most recent releases at:https://pypi.python.org/pypi/deap/.DocumentationSee theDEAP User's Guidefor DEAP documentation.In order to get the tip documentation, change directory to thedocsubfolder and type inmake html, the documentation will be under_build/html. You will needSphinxto build the documentation.NotebooksAlso checkout our newnotebook examples. UsingJupyter notebooksyou'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 runjupyternotebookInstallationWe 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 usually provide an outdated version.pipinstalldeapThe latest version can be installed withpipinstallgit+https://github.com/DEAP/deap@masterIf you wish to build from sources, download or clone the repository and typepythonsetup.pyinstallBuild StatusDEAP build status is available on Travis-CIhttps://travis-ci.org/DEAP/deap.RequirementsThe 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, however this requires havingsetuptools<=58. It is recommended to usepip install setuptools==57.5.0to address this issue.ExampleThe following code gives a quick overview how simple it is to implement the Onemax problem optimization with genetic algorithm using DEAP.  More examples are providedhere.importrandomfromdeapimportcreator,base,tools,algorithmscreator.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)defevalOneMax(individual):returnsum(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=40forgeninrange(NGEN):offspring=algorithms.varAnd(population,toolbox,cxpb=0.5,mutpb=0.1)fits=toolbox.map(toolbox.evaluate,offspring)forfit,indinzip(fits,offspring):ind.fitness.values=fitpopulation=toolbox.select(offspring,k=len(population))top10=tools.selBest(population,k=10)How to cite DEAPAuthors of scientific papers including results generated using DEAP are encouraged to cite the following paper.@article{DEAP_JMLR2012,author="F\'elix-AntoineFortinandFran\c{c}ois-Michel{DeRainville}andMarc-Andr\'eGardnerandMarcParizeauandChristianGagn\'e",title={{DEAP}:EvolutionaryAlgorithmsMadeEasy},pages={2171--2175},volume={13},month={jul},year={2012},journal={JournalofMachineLearningResearch}
}Publications on DEAPFrançois-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.PaperFélix-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.PaperFrançois-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.PaperProjects using DEAPRibaric, 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.2016.00017https://github.com/BlueBrain/BluePyOptLara-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 inTPOT, an open source tool that uses genetic programming to optimize machine learning pipelines.DEAP is also used in ROS as an optimization packagehttp://www.ros.org/wiki/deap.DEAP is an optional dependency forPyXRD, a Python implementation of the matrix algorithm developed for the X-ray diffraction analysis of disordered lamellar structures.DEAP is used inglyph, a library for symbolic regression with applications toMLC.DEAP is used inSklearn-genetic-opt, an open source tool that uses evolutionary programming to fine tune machine learning hyperparameters.If you want your project listed here, send us a link and a brief description and we'll be glad to add it.