def generator(self, constants): """ Carries out the standard evolutionary process of parent selection, evolution, and survivor selection. Parameters: -``constants``: Config settings for the EA from the config files specified at run time """ for individual in self.initialIndividuals(constants): yield individual while True: if constants["logAvgFitness"]: print self.getAvgFitness() parents = self.parentSelection(self.individuals, constants["offSize"] * constants["parentsPerChild"], constants) for i in range(0, len(parents), constants["parentsPerChild"]): family = parents[i:i + constants["parentsPerChild"]] child = Individual(id=id) child.parents = [parent.id for parent in family] self.id += 1 self.crossover(child, family, constants) self.mutation(child, constants, family) self.geneType.fix(child.genes) yield child self.individuals.append(child) self.individuals = self.survivorSelection(self.individuals, constants["popSize"], constants)
def generator(self, constants): """ Carries out the standard evolutionary process of parent selection, evolution, and survivor selection. Parameters: -``constants``: Config settings for the EA from the config files specified at run time """ for _ in self.initialIndividuals(constants): pass for individual in self.evalPop(constants): yield individual while True: parents = self.parentSelection(self.individuals, constants["offSize"] * constants["parentsPerChild"], constants) for i in range(0, len(parents), constants["parentsPerChild"]): family = parents[i:i + constants["parentsPerChild"]] child = Individual(id=id) child.parents = [parent.id for parent in family] self.id += 1 child.crossover = self.crossover(family[0].crossover, family[1].crossover, constants) self.mutation(child, constants, family) self.individuals.append(child) for individual in self.evalPop(constants): yield individual self.individuals = self.survivorSelection(self.individuals, constants["popSize"], constants)
def generator(self, constants):
"""
Carries out the standard evolutionary process of parent selection, evolution, and survivor selection.
Parameters:
-``constants``: Config settings for the EA from the config files specified at run time
"""
for individual in self.initialIndividuals(constants):
yield individual
while True:
if constants["logAvgFitness"]:
print self.getAvgFitness()
parents = self.parentSelection(
self.individuals,
constants["offSize"] * constants["parentsPerChild"], constants)
for i in range(0, len(parents), constants["parentsPerChild"]):
family = parents[i:i + constants["parentsPerChild"]]
child = Individual(id=id)
child.parents = [parent.id for parent in family]
self.id += 1
self.crossover(child, family, constants)
self.mutation(child, constants, family)
self.geneType.fix(child.genes)
yield child
self.individuals.append(child)
self.individuals = self.survivorSelection(self.individuals,
constants["popSize"],
constants)
def generator(self, constants):
"""
Carries out the standard evolutionary process of parent selection, evolution, and survivor selection.
Parameters:
-``constants``: Config settings for the EA from the config files specified at run time
"""
for _ in self.initialIndividuals(constants):
pass
for individual in self.evalPop(constants):
yield individual
while True:
parents = self.parentSelection(
self.individuals,
constants["offSize"] * constants["parentsPerChild"], constants)
for i in range(0, len(parents), constants["parentsPerChild"]):
family = parents[i:i + constants["parentsPerChild"]]
child = Individual(id=id)
child.parents = [parent.id for parent in family]
self.id += 1
child.crossover = self.crossover(family[0].crossover,
family[1].crossover,
constants)
self.mutation(child, constants, family)
self.individuals.append(child)
for individual in self.evalPop(constants):
yield individual
self.individuals = self.survivorSelection(self.individuals,
constants["popSize"],
constants)
def generator(self, constants, supports=None): """ Carries out the standard evolutionary process of parent selection, evolution, and survivor selection. Parameters: -``constants``: Config settings for the EA from the config files specified at run time """ supportIndividual = None for individual in self.initialIndividuals(constants): if "MutationCoPopulation" not in constants["supportPopulations"]: # TODO Explain ranging SAfix = GeneTypes.FLT(0.001, constants["mutationStepSize"]) individual.stepSizes = SAfix.randomGenome(constants["dimensions"]) yield individual, None while True: if constants["logAvgFitness"]: print self.getAvgFitness() #print "generation" parents = self.parentSelection(self.individuals, constants["offSize"] * constants["parentsPerChild"], constants) for i in range(0, len(parents), constants["parentsPerChild"]): family = parents[i:i + constants["parentsPerChild"]] child = Individual(id=id) child.parents = [parent for parent in family] self.id += 1 #self.crossover(child, family, constants) supportIndividuals = list() if "MutationCoPopulation" in constants["supportPopulations"]: mutationSupportIndividual = supports[MutationCoPopulation]() #mutationSupport() rates = mutationSupportIndividual.genes supportIndividuals.append(mutationSupportIndividual) """ for r in rates: if r != constants["mutationStepSize"]: print "rates changed after creation" """ #print "mutation created" elif "MutationCoPopulation" not in constants["supportPopulations"]: bias = 1 / math.sqrt(2.0 * constants["dimensions"]) * random.gauss(0, 1) tau = 1 / math.sqrt(2.0 * math.sqrt(constants["dimensions"])) child.stepSizes = [(sum(psteps) / len(psteps)) * math.exp(bias + tau * random.gauss(0, 1)) for psteps in zip(*[f.stepSizes for f in family])] SAfix.fix(child.stepSizes) rates = child.stepSizes if "SCXCoPopulation" in constants["supportPopulations"]: crossoverSupportIndividual = supports[SCXCoPopulation]() #crossoverSupport() #print len(crossoverSupportIndividual.crossover.genes) Crossover.scxFromSupport(child, parents, crossoverSupportIndividual, constants) #print len(crossoverSupportIndividual.crossover.genes) supportIndividuals.append(crossoverSupportIndividual) #print "crossover created" elif "SCXCoPopulation" not in constants["supportPopulations"]: self.crossover(child, family, constants) #print len(child.crossover.genes) if constants["SuCoLevel"] == "Static": rates = constants["stepSizes"] """ for r in rates: if r != constants["mutationStepSize"]: print "rates changed" """ self.mutation(child, constants, rates) self.geneType.fix(child.genes) yield child, supportIndividuals self.individuals.append(child) self.individuals = self.survivorSelection(self.individuals, constants["popSize"], constants)
def generator(self, constants, supports=None):
"""
Carries out the standard evolutionary process of parent selection, evolution, and survivor selection.
Parameters:
-``constants``: Config settings for the EA from the config files specified at run time
"""
supportIndividual = None
for individual in self.initialIndividuals(constants):
if "MutationCoPopulation" not in constants["supportPopulations"]:
# TODO Explain ranging
SAfix = GeneTypes.FLT(0.001, constants["mutationStepSize"])
individual.stepSizes = SAfix.randomGenome(
constants["dimensions"])
yield individual, None
while True:
if constants["logAvgFitness"]:
print self.getAvgFitness()
#print "generation"
parents = self.parentSelection(
self.individuals,
constants["offSize"] * constants["parentsPerChild"], constants)
for i in range(0, len(parents), constants["parentsPerChild"]):
family = parents[i:i + constants["parentsPerChild"]]
child = Individual(id=id)
child.parents = [parent for parent in family]
self.id += 1
#self.crossover(child, family, constants)
supportIndividuals = list()
if "MutationCoPopulation" in constants["supportPopulations"]:
mutationSupportIndividual = supports[MutationCoPopulation](
) #mutationSupport()
rates = mutationSupportIndividual.genes
supportIndividuals.append(mutationSupportIndividual)
"""
for r in rates:
if r != constants["mutationStepSize"]:
print "rates changed after creation"
"""
#print "mutation created"
elif "MutationCoPopulation" not in constants[
"supportPopulations"]:
bias = 1 / math.sqrt(
2.0 * constants["dimensions"]) * random.gauss(0, 1)
tau = 1 / math.sqrt(
2.0 * math.sqrt(constants["dimensions"]))
child.stepSizes = [
(sum(psteps) / len(psteps)) *
math.exp(bias + tau * random.gauss(0, 1))
for psteps in zip(*[f.stepSizes for f in family])
]
SAfix.fix(child.stepSizes)
rates = child.stepSizes
if "SCXCoPopulation" in constants["supportPopulations"]:
crossoverSupportIndividual = supports[SCXCoPopulation](
) #crossoverSupport()
#print len(crossoverSupportIndividual.crossover.genes)
Crossover.scxFromSupport(child, parents,
crossoverSupportIndividual,
constants)
#print len(crossoverSupportIndividual.crossover.genes)
supportIndividuals.append(crossoverSupportIndividual)
#print "crossover created"
elif "SCXCoPopulation" not in constants["supportPopulations"]:
self.crossover(child, family, constants)
#print len(child.crossover.genes)
if constants["SuCoLevel"] == "Static":
rates = constants["stepSizes"]
"""
for r in rates:
if r != constants["mutationStepSize"]:
print "rates changed"
"""
self.mutation(child, constants, rates)
self.geneType.fix(child.genes)
yield child, supportIndividuals
self.individuals.append(child)
self.individuals = self.survivorSelection(self.individuals,
constants["popSize"],
constants)