DEAP (software)
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 | This article needs additional citations for verification. (September 2015) |
| 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 | 1.4.1[1]  / 21 July 2023 |
| 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.[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.
Example[edit]
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[edit]
References[edit]
- ^ "Release 1.4.1". 21 July 2023. Retrieved 30 July 2023.
- ^ 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.
- ^ 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. doi:10.1145/2597453.2597455. S2CID 14949980.
- ^ 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.
- ^ "Creation of one algorithm to manage traffic systems". Social Impact Open Repository. Archived from the original on 2017-09-05. Retrieved 2017-09-05.