DEAP (software)

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.

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 release2009 (2009)
Stable release
1.2.2 / November 12, 2017 (2017-11-12)
Repository
Written inPython
Operating systemCross-platform
TypeEvolutionary computation framework
LicenseLGPL
Websitegithub.com/deap

Example

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

import array
import 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.
  4. "Creation of one algorithm to manage traffic systems". Social Impact Open Repository. Archived from the original on 2017-09-05. Retrieved 2017-09-05.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.