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## 3.7 Variations
import random
from deap import base
from deap import creator
from deap import tools
## Data structure and initializer creation
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("attr_float", random.random)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, 10)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
def onemax(individual):
return sum(individual),
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2)
toolbox.register("select", tools.selTournament, tournsize=3)
toolbox.register("evaluate", onemax)
pop = toolbox.population(n=100)
CXPB, MUTPB, NGEN= 0.7, 0.3, 25
fitnesses = toolbox.map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
from deap import algorithms
for g in range(NGEN):
# Select and clone the next generation individuals
offspring = map(toolbox.clone, toolbox.select(pop, len(pop)))
# Apply crossover and mutation on the offspring
offspring = algorithms.varAnd(offspring, toolbox, CXPB, MUTPB)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# The population is entirely replaced by the offspring
pop[:] = offspring
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