DEAP

DEAPOriginal author(s)| François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner, Marc Parizeau, Christian Gagné

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Developer(s)| François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner
Initial release| 2009 (2009)
Written in| Python
Operating system| Cross-platform
Type| Evolutionary computation framework
License| LGPL
Website| github.com/deap

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.

## Contents

* 1 Example
* 2 See also
* 3 References
* 4 External links

## 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

## References

1. ↑ 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. http://jmlr.org/papers/v13/fortin12a.html.
2. ↑ De Rainville, François-Michel; F.-A Fortin; M-A. Gardner; C. Gagné; M. Parizeau (2014). "DEAP: Enabling Nimber Evolutionss". SIGEvolution 6 (2): 17–26. doi:10.1145/2597453.2597455. http://www.sigevolution.org/issues/pdf/SIGEVOlution0602.pdf.
3. ↑ De Rainville, François-Michel; F.-A Fortin; M-A. Gardner; C. Gagné; M. Parizeau (2012). "DEAP: A Python Framework for Evolutionary Algorithms". In Companion Proceedings of the Genetic and Evolutionary Computation Conference. http://vision.gel.ulaval.ca/~cgagne/pubs/deap-gecco-2012.pdf.
4. ↑ "Creation of one algorithm to manage traffic systems". http://sior.ub.edu/jspui/cris/socialimpact/socialimpact00441.

## External links

* on GitHub

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