| Distributed Evolutionary Algorithms in Python | |
| DEAP | |
| Author: | François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner, Marc Parizeau, Christian Gagné |
| Developer: | François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner |
| Programming Language: | Python |
| Operating System: | Cross-platform |
| Genre: | Evolutionary computation framework |
| License: | LGPL |
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.
The following code gives a quick overview how the Onemax problem optimization with genetic algorithm can be implemented with DEAP.
creator.create("FitnessMax", base.Fitness, weights=(1.0,))creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax)
toolbox = base.Toolboxtoolbox.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