def main():
random.seed(1024)
ind = toolbox.individual()
pop = toolbox.population(n=100)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", tools.mean)
stats.register("std", tools.std)
stats.register("min", min)
stats.register("max", max)
column_names = ["gen", "evals"]
column_names.extend(stats.functions.keys())
logger = tools.EvolutionLogger(column_names)
logger.logHeader()
CXPB, MUTPB, NGEN = 0.5, 0.2, 40
# Evaluate the entire population
for ind in pop:
ind.fitness.values = toolbox.evaluate(ind)
hof.update(pop)
stats.update(pop)
logger.logGeneration(gen=0, evals=len(pop), stats=stats)
for g in range(1, NGEN):
# Select the offspring
offspring = toolbox.select(pop, len(pop))
# Clone the offspring
offspring = [toolbox.clone(ind) for ind in offspring]
# Apply crossover and mutation
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
for tree1, tree2 in zip(ind1, ind2):
if random.random() < CXPB:
toolbox.mate(tree1, tree2)
del ind1.fitness.values
del ind2.fitness.values
for ind in offspring:
for tree in ind:
if random.random() < MUTPB:
toolbox.mutate(tree)
del ind.fitness.values
# Evaluate the individuals with an invalid fitness
invalids = [ind for ind in offspring if not ind.fitness.valid]
for ind in invalids:
ind.fitness.values = toolbox.evaluate(ind)
# Replacement of the population by the offspring
pop = offspring
hof.update(pop)
stats.update(pop)
logger.logGeneration(gen=g, evals=len(invalids), stats=stats)
print('Best individual : ', gp.stringify(hof[0][0]), hof[0].fitness)
return pop, stats, hof
def main():
pop_ga = toolbox_ga.population(n=200)
pop_gp = tools_gp.population(n=200)
stats_ga = tools.Statistics(lambda ind: ind.fitness.values)
stats_ga.register("avg", tools.mean)
stats_ga.register("std", tools.std)
stats_ga.register("min", min)
stats_ga.register("max", max)
stats_gp = tools.Statistics(lambda ind: ind.fitness.values)
stats_gp.register("avg", tools.mean)
stats_gp.register("std", tools.std)
stats_gp.register("min", min)
stats_gp.register("max", max)
column_names = ["gen", "evals"]
column_names.extend(stats_ga.functions.keys())
logger = tools.EvolutionLogger(column_names)
logger.logHeader()
best_ga = tools.selRandom(pop_ga, 1)[0]
best_gp = tools.selRandom(pop_gp, 1)[0]
for ind in pop_gp:
ind.fitness.values = evalSymbReg(ind, best_ga)
for ind in pop_ga:
ind.fitness.values = evalSymbReg(best_gp, ind)
stats_ga.update(pop_ga)
stats_gp.update(pop_gp)
logger.logGeneration(gen="0 (ga)", evals=len(pop_ga), stats=stats_ga)
logger.logGeneration(gen="0 (gp)", evals=len(pop_gp), stats=stats_gp)
CXPB, MUTPB, NGEN = 0.5, 0.2, 50
# Begin the evolution
for g in range(1, NGEN):
# Select and clone the offspring
off_ga = toolbox_ga.select(pop_ga, len(pop_ga))
off_gp = tools_gp.select(pop_gp, len(pop_gp))
off_ga = [toolbox_ga.clone(ind) for ind in off_ga]
off_gp = [tools_gp.clone(ind) for ind in off_gp]
# Apply crossover and mutation
for ind1, ind2 in zip(off_ga[::2], off_ga[1::2]):
if random.random() < CXPB:
toolbox_ga.mate(ind1, ind2)
del ind1.fitness.values
del ind2.fitness.values
for ind1, ind2 in zip(off_gp[::2], off_gp[1::2]):
if random.random() < CXPB:
tools_gp.mate(ind1, ind2)
del ind1.fitness.values
del ind2.fitness.values
for ind in off_ga:
if random.random() < MUTPB:
toolbox_ga.mutate(ind)
del ind.fitness.values
for ind in off_gp:
if random.random() < MUTPB:
tools_gp.mutate(ind)
del ind.fitness.values
# Evaluate the individuals with an invalid fitness
for ind in off_ga:
ind.fitness.values = evalSymbReg(best_gp, ind)
for ind in off_gp:
ind.fitness.values = evalSymbReg(ind, best_ga)
# Replace the old population by the offspring
pop_ga = off_ga
pop_gp = off_gp
stats_ga.update(pop_ga)
stats_gp.update(pop_gp)
best_ga = tools.selBest(pop_ga, 1)[0]
best_gp = tools.selBest(pop_gp, 1)[0]
logger.logGeneration(gen="%d (ga)" % g, evals=len(off_ga), stats=stats_ga)
logger.logGeneration(gen="%d (gp)" % g, evals=len(off_gp), stats=stats_gp)
print("Best individual GA is %s, %s" % (best_ga, best_ga.fitness.values))
print("Best individual GP is %s, %s" % (gp.stringify(best_gp), best_gp.fitness.values))
return pop_ga, pop_gp, stats_ga, stats_gp, best_ga, best_gp
def generateWebService(self,individual):
""" Generates a file called 'clonedwebservice.py' that contains a webservice with the specified individual"""
generateService(self.configuration.imports,
getSignature(self.configuration.testArguments),
self.lambdify(gp.stringify(individual),self.configuration.pset),
self.configuration.configClass,)
def generateSource(self,individual):
"""Takes all the information from the configuration file and represents the source of the individual """
source = self.configuration.configClass.getSource(self.configuration.imports,
self.lambdify(gp.stringify(individual),self.configuration.pset),
getSignature(self.configuration.testArguments))
return source
def outputIndividuals(self):
inds = []
for ind in self.hof:
inds.append((gp.stringify(ind), ind.fitness))
return inds
