def sapienz_mut_suite(self, individual: Individual,
indpb: float) -> Tuple[Individual]:
# shuffle seq
individual, = tools.mutShuffleIndexes(individual, indpb)
# crossover inside the suite
# perform one point crossover between odd and even pair positions of test cases
for i in range(1, len(individual), 2):
if random.random() < settings.MUTPB:
if len(individual[i - 1]) <= 2:
continue
if len(individual[i]) <= 2:
continue
individual[i - 1], individual[i] = tools.cxOnePoint(
individual[i - 1], individual[i])
# shuffle events inside each test case
for i in range(len(individual)):
if random.random() < settings.MUTPB:
if len(individual[i]) <= 2:
continue
individual[i], = tools.mutShuffleIndexes(individual[i], indpb)
return individual,
def mut_suite(individual, indpb):
# shuffle seq
individual, = tools.mutShuffleIndexes(individual, indpb)
# crossover inside the suite
for i in range(1, len(individual), 2):
if random.random() < settings.MUTPB:
if len(individual[i - 1]) <= 2:
print "\n\n### Indi Length =", len(individual[i - 1]), " ith = ", i - 1, individual[i - 1]
continue # sys.exit(1)
if len(individual[i]) <= 2:
print "\n\n### Indi Length =", len(individual[i]), "ith = ", i, individual[i]
continue # sys.exit(1)
individual[i - 1], individual[i] = tools.cxOnePoint(individual[i - 1], individual[i])
# individual[i - 1], individual[i] = tools.cxUniform(individual[i - 1], individual[i], indpb=0.5)
# shuffle events
for i in range(len(individual)):
if random.random() < settings.MUTPB:
if len(individual[i]) <= 2:
print "\n\n### Indi Length =", len(individual[i]), "ith = ", i, individual[i]
continue # sys.exit(1)
individual[i], = tools.mutShuffleIndexes(individual[i], indpb)
return individual,
def mut_suite(individual, indpb): # shuffle seq individual, = tools.mutShuffleIndexes(individual, indpb) # crossover inside the suite for i in range(1, len(individual), 2): if random.random() < settings.MUTPB: if len(individual[i - 1]) <= 2: print "\n\n### Indi Length =", len(individual[i - 1]), " ith = ", i - 1, individual[i - 1] continue # sys.exit(1) if len(individual[i]) <= 2: print "\n\n### Indi Length =", len(individual[i]), "ith = ", i, individual[i] continue # sys.exit(1) individual[i - 1], individual[i] = tools.cxOnePoint(individual[i - 1], individual[i]) # shuffle events for i in range(len(individual)): if random.random() < settings.MUTPB: if len(individual[i]) <= 2: print "\n\n### Indi Length =", len(individual[i]), "ith = ", i, individual[i] continue # sys.exit(1) individual[i], = tools.mutShuffleIndexes(individual[i], indpb) return individual,
def mutate_permutation(individual, solver):
method, parameters = solver.current_step.method, solver.current_step.parameters
# print("Before: {}".format(individual))
if method in (SolverMethod.CLUSTERING, SolverMethod.BOTH):
# mutate_assignment(individual, solver, parameters['inpdb'])
for item in individual[:-1]:
tools.mutShuffleIndexes(item, parameters['inpdb'])
if method in (SolverMethod.SCHEDULING, SolverMethod.BOTH):
tools.mutShuffleIndexes(individual[-1], parameters['inpdb'])
# print("After: {}".format(individual))
return individual,
def __mutate(ind, indpb):
"""
该函数为变异函数，对ind执行变异操作。
在Deap中，可以使用的函数只有mutShuffleIndexes。
具体查看https://deap.readthedocs.io/en/master/api/tools.html#operators
"""
return tools.mutShuffleIndexes(ind, indpb)
def mutate(self, individual, probability):
if self.method == 'Polynomial':
return tools.mutPolynomialBounded(individual,
eta=20.0,
low=0.0,
up=1.0,
indpb=1 / len(individual))[0]
elif self.method == 'Shuffle':
return tools.mutShuffleIndexes(individual, probability)[0]
def mutation(ind,mu,sigma,pr,ll,ul):
for i in xrange(len(ind[0])):
if random.random() < pr:
ind[0][i] += int(math.ceil(random.gauss(mu,sigma)))
if ind[0][i] < 0:
ind[0][i] = 0
if random.random() < pr:
ind[1] = tools.mutShuffleIndexes(ind[1],pr)[0]
for i in xrange(len(ind[2])):
if random.random() < pr:
ind[2][i] = random.randint(ll,ul)
return ind,
def mutate(individual, indpb):
# shuffle seq
individual, = tools.mutShuffleIndexes(individual, indpb)
# crossover inside the suite
for i in range(1, len(list(individual)), 2):
if random.random() < MUTPB:
if len(list(individual)) <= 2:
continue # sys.exit(1)
if len(list(individual)) <= 2:
continue # sys.exit(1)
individual[i - 1], individual[i] = tools.cxBlend(
individual[i - 1], individual[i], 0.7)
# shuffle events
for i in range(len(list(individual))):
if random.random() < MUTPB:
if len(list(individual)) <= 2:
continue # sys.exit(1)
list(individual)[i], = tools.mutShuffleIndexes(
list(individual)[i], indpb)
return individual
def custom_mutate(ind, indpb):
i = random.randint(1, 3)
if i == 1:
tools.mutShuffleIndexes(ind[0], indpb)
elif i == 2:
tools.mutShuffleIndexes(ind[1], indpb)
elif i == 3:
tools.mutShuffleIndexes(ind[2], indpb)
return ind,
def mutation(ind, avail, mis, pr, ll, ul):
nc = len(ind[0])
j = random.randint(0, int(nc / 3))
for ij in range(j):
ind[0][random.randint(0, nc - 1)] = random.randint(0, avail / 2)
while sum(ind[0]) > avail:
ind[0] = [int(x / 2.0) for x in ind[0]]
ind[0][0] = max(mis, ind[0][0])
if random.random() < pr:
ind[1] = tools.mutShuffleIndexes(ind[1], pr)[0]
if ul > ll:
j = random.randint(0, int(nc / 3))
for ij in range(j):
if random.random() < pr:
ind[2][random.randint(0, nc - 1)] = random.randint(ll, ul)
return (ind,)
def mutation(ind, avail, mis, pr, ll, ul):
nc = len(ind[0])
j = random.randint(0, int(nc / 3))
for ij in range(j):
ind[0][random.randint(0, nc - 1)] = random.randint(0, avail / 2)
while sum(ind[0]) > avail:
ind[0] = [int(x / 2.) for x in ind[0]]
ind[0][0] = max(mis, ind[0][0])
if random.random() < pr:
ind[1] = tools.mutShuffleIndexes(ind[1], pr)[0]
if ul > ll:
j = random.randint(0, int(nc / 3))
for ij in range(j):
if random.random() < pr:
ind[2][random.randint(0, nc - 1)] = random.randint(ll, ul)
return ind,
def mutate(x, indpb):
for x_attr in x:
tools.mutShuffleIndexes(x_attr, indpb)
def mutation_main(individual, indpb, column_names, heating_unit_names_share,
cooling_unit_names_share, column_names_buildings_heating,
column_names_buildings_cooling, district_heating_network,
district_cooling_network):
# create dict of individual with his/her name
individual_with_name_dict = dict(zip(column_names, individual))
if district_heating_network:
# MUTATE BUILDINGS CONNECTED
buildings_heating = [
individual_with_name_dict[column]
for column in column_names_buildings_heating
]
# apply mutations
buildings_heating_mutated = tools.mutFlipBit(buildings_heating,
indpb)[0]
# take back to the individual
for column, mutated_value in zip(column_names_buildings_heating,
buildings_heating_mutated):
individual_with_name_dict[column] = mutated_value
# MUTATE SUPPLY SYSTEM UNITS SHARE
heating_units_share = [
individual_with_name_dict[column]
for column in heating_unit_names_share
]
# apply mutations
heating_units_share_mutated = tools.mutShuffleIndexes(
heating_units_share, indpb=indpb)[0]
# takeback to teh individual
for column, mutated_value in zip(heating_unit_names_share,
heating_units_share_mutated):
individual_with_name_dict[column] = mutated_value
if district_cooling_network:
# MUTATE BUILDINGS CONNECTED
buildings_cooling = [
individual_with_name_dict[column]
for column in column_names_buildings_cooling
]
# apply mutations
buildings_cooling_mutated = tools.mutFlipBit(buildings_cooling,
indpb)[0]
# take back to teh individual
for column, mutated_value in zip(column_names_buildings_cooling,
buildings_cooling_mutated):
individual_with_name_dict[column] = mutated_value
# MUTATE SUPPLY SYSTEM UNITS SHARE
cooling_units_share = [
individual_with_name_dict[column]
for column in cooling_unit_names_share
]
# apply mutations
cooling_units_share_mutated = tools.mutShuffleIndexes(
cooling_units_share, indpb)[0]
# takeback to teh individual
for column, mutated_value in zip(cooling_unit_names_share,
cooling_units_share_mutated):
individual_with_name_dict[column] = mutated_value
# now validate individual
individual_with_name_dict = validation_main(
individual_with_name_dict, column_names_buildings_heating,
column_names_buildings_cooling, district_heating_network,
district_cooling_network)
# now pass all the values mutated to the original individual
for i, column in enumerate(column_names):
individual[i] = individual_with_name_dict[column]
return individual, # add the, because deap needs this
def mutate(ind, all_relays=None):
_ind = ind.clone()
return tools.mutShuffleIndexes(_ind, 0.2)[0]
def mutate(self, individual, probability):
if self.method == 'Shuffle':
return tools.mutShuffleIndexes(individual, probability)[0]
elif self.method == 'Flipbit':
return tools.mutFlipBit(individual, probability)[0]
def mixed_mutate(individual, indpb):
tools.mutShuffleIndexes(individual[0], indpb)
toolsx.mut_uniform_unique(individual[1], 1, len(df_service) - 1, 0.2)
return individual,
def tspd_mutation(ind, indpb_cities, indpb_drone_visiting):
tools.mutShuffleIndexes(ind[0], indpb_cities)
tools.mutFlipBit(ind[1], indpb_drone_visiting)
return ind,
import random
from deap import tools
random.seed(0)
x = random.sample(range(10), 10)
print(x)
print(tools.mutShuffleIndexes(x, 1))
print(x)
ind1 = random.sample(range(10), 10)
ind2 = random.sample(range(10), 10)
tools.cxTwoPoint(ind1, ind2)
ind = [ind1, ind2]
tools.cxOrdered(ind1, ind2)
for i in range(100):
ind1 = random.sample(range(10), 10)
ind2 = random.sample(range(10), 10)
tools.mutShuffleIndexes(ind1, 1)
print(ind1)
for i in range(100):
ind1 = random.sample(range(10), 10)
tools.mutUniformInt(ind1, 20, 100, 1)
print(ind1)
for i in range(100):
def fixedActivationMut(individual, MINPDB):
return tools.mutShuffleIndexes(individual, MINPDB)