DEAP (software)

This article is about the software framework for evolutionary computation. For other uses, see DEAP (disambiguation).
DEAP
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 release 2009 (2009)
Stable release 1.0.0 / February 20, 2014 (2014-02-20)
Development status Active
Written in Python
Operating system Cross-platform
Type Evolutionary computation framework
License LGPL
Website github.com/deap

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 and estimation of distribution algorithm. It is developed at Université Laval since 2009.

Example

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)

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 = offspring

See also

References

  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.

External links

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