DEAP

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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.

DEAP includes the following features:

- Genetic algorithm using any imaginable representation
  - List, Array, Set, Dictionary, Tree, Numpy Array, etc.
- Genetic programming using prefix trees
  - Loosely typed, Strongly typed
  - Automatically defined functions
- Evolution strategies (including CMA-ES)
- Multi-objective optimisation (NSGA-II, NSGA-III, 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)
- Examples of alternative algorithms : Particle Swarm Optimization,
  Differential Evolution, Estimation of Distribution Algorithm

Downloads

Following acceptance of PEP 438 by the Python community, we have moved
DEAP’s source releases on PyPI.

You can find the most recent releases at:
https://pypi.python.org/pypi/deap/.

Documentation

See the DEAP User’s Guide 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 to build the documentation.

Notebooks

Also checkout our new notebook examples. Using Jupyter notebooks 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

    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provide an outdated version.

    pip install deap

The latest version can be installed with

    pip install git+https://github.com/DEAP/deap@master

If you wish to build from sources, download or clone the repository and
type

    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, however this requires having setuptools<=58. It is
recommended to use pip install setuptools==57.5.0 to address this issue.

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.

    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.

    @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
- 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
- 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

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, 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, a Python implementation of
  the matrix algorithm developed for the X-ray diffraction analysis of
  disordered lamellar structures.
- DEAP is used in glyph, a library for symbolic regression with
  applications to MLC.
- DEAP is used in Sklearn-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.
