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DEAP (software)

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Distributed Evolutionary Algorithms in Python (DEAP) is an evolutionary computation framework for rapid prototyping and testing of ideas.[2][3][4] 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[5] and estimation of distribution algorithm. It is developed at Université Laval since 2009.

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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
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See also

References

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