DEAP (software)

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.

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	1.4.1[1]  / 21 July 2023; 22 months ago (21 July 2023)
Repository	github.com/DEAP/deap
Written in	Python
Operating system	Cross-platform
Type	Evolutionary computation framework
License	LGPL
Website	github.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

• Python SCOOP (software)
• Free software portal