| 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 |
| Stable release | 1.3.3 
/ 8 August 2022
|
| Repository | |
| Written in | Python |
| Operating system | Cross-platform |
| Type | Evolutionary computation framework |
| License | LGPL |
| Website | github |
Distributed Evolutionary Algorithms in Python (DEAP) is an evolutionary computation framework for rapid prototyping and testing of ideas. 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 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
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
