DEAP (software)
This article is about the software framework for evolutionary computation. For other uses, see DEAP (disambiguation).
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 |
1.0.2
/ May 13, 2015 |
Development status | Active |
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 .[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 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, 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
References
- ↑ 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.
- ↑ 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.
External links
This article is issued from Wikipedia - version of the 6/22/2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.