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import operator
import random
import numpy
from deap import algorithms
from deap import base
from deap import creator
from deap import gp
from deap import tools
random.seed(0)
stats_fit = tools.Statistics(key=lambda ind: ind.fitness.values)
stats_size = tools.Statistics(key=len)
mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)
mstats.register("avg", numpy.mean)
mstats.register("std", numpy.std)
mstats.register("min", numpy.min)
mstats.register("max", numpy.max)
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin)
pset = gp.PrimitiveSet("MAIN", 1)
pset.addPrimitive(operator.add, 2)
pset.addPrimitive(operator.mul, 2)
pset.addEphemeralConstant("rand101", lambda: random.randint(-1,1))
pset.renameArguments(ARG0='x')
def evalSymbReg(individual, points):
# Transform the tree expression in a callable function
func = toolbox.compile(expr=individual)
# Evaluate the mean squared error between the expression
# and the real function : x**4 + x**3 + x**2 + x
sqerrors = ((func(x) - x**4 - x**3 - x**2 - x)**2 for x in points)
return sum(sqerrors) / len(points),
toolbox = base.Toolbox()
toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=1, max_=2)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", evalSymbReg, points=[x/10. for x in range(-10,10)])
toolbox.register("compile", gp.compile, pset=pset)
pop = toolbox.population(n=100)
# Evaluate the individuals
fitnesses = map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
record = mstats.compile(pop)
print(record)
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