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Distributed Evolutionary Algorithms in Python (DEAP) is an evolutionary computation framework for rapid prototyping and testing of ideas.[1][2][3] It incorporates the data structures and tools required to implement most common evolutionary computation techniques such as genetic algorithm, genetic programming, evolution strategies, particle swarm optimization, differential evolution, traffic flow[4] and estimation of distribution algorithm. It is developed at Université Laval since 2009.

Original author(s)François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner, Marc Parizeau, Christian Gagné
Developer(s)François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner
Initial release2009 (2009)
Stable release
1.2.2 / November 12, 2017; 2 years ago (2017-11-12)
Repository Edit this at Wikidata
Written inPython
Operating systemCross-platform
TypeEvolutionary computation framework


The following code gives a quick overview how the Onemax problem optimization with genetic algorithm can be implemented with DEAP.

import array, random
from deap import creator, base, tools, algorithms

creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax)

toolbox = base.Toolbox()

toolbox.register("attr_bool", random.randint, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)

evalOneMax = lambda individual: (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)

for gen in range(NGEN):
    offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)
    fits =, offspring)
    for fit, ind in zip(fits, offspring): = fit
    population = offspring

See alsoEdit


  1. ^ Fortin, Félix-Antoine; F.-M. De Rainville; M-A. Gardner; C. Gagné; M. Parizeau (2012). "DEAP: Evolutionary Algorithms Made Easy". Journal of Machine Learning Research. 13: 2171–2175.
  2. ^ De Rainville, François-Michel; F.-A Fortin; M-A. Gardner; C. Gagné; M. Parizeau (2014). "DEAP: Enabling Nimber Evolutionss" (PDF). SIGEvolution. 6 (2): 17–26.
  3. ^ De Rainville, François-Michel; F.-A Fortin; M-A. Gardner; C. Gagné; M. Parizeau (2012). "DEAP: A Python Framework for Evolutionary Algorithms" (PDF). In Companion Proceedings of the Genetic and Evolutionary Computation Conference.
  4. ^ "Creation of one algorithm to manage traffic systems". Social Impact Open Repository. Archived from the original on 2017. Retrieved 2017-09-05.

External linksEdit