def main():
# random.seed(0)
# numpy.random.seed(0)
string_length = 8
popsize = 20 # original 10
mating_pool_size = int(popsize * 0.5) # has to be even
mut_rate = 0.1 # original 1
xover_rate = 0.8 # original 0.9
# initialize population
population = initialization.permutation(popsize, string_length)
fitness = []
print("Initial population:")
for i in range(0, popsize):
fitness.append(evaluation.fitness_8queen(population[i]))
print(i, ":", population[i], "fitness:", fitness[i])
# pick parents
parents_index = random.sample(range(0, popsize), mating_pool_size)
print("Parents:")
for i in range(0, len(parents_index)):
print(parents_index[i], ":", population[parents_index[i]])
# reproduction
offspring = []
i = 0
# offspring are generated using selected parents in the mating pool
while len(offspring) < mating_pool_size:
# recombination
if random.random() < xover_rate:
off1, off2 = recombination.permutation_cut_and_crossfill(
population[parents_index[i]], population[parents_index[i + 1]])
else:
off1 = population[parents_index[i]].copy()
off2 = population[parents_index[i + 1]].copy()
# print offspring after recombination
print("After xover:")
print(off1, "fitness:", evaluation.fitness_8queen(off1))
print(off2, "fitness:", evaluation.fitness_8queen(off2))
if random.random() < mut_rate:
off1 = mutation.permutation_swap(off1)
if random.random() < mut_rate:
off2 = mutation.permutation_swap(off2)
# print offspring after mutation
print("After mutation:")
print(off1, "fitness:", evaluation.fitness_8queen(off1))
print(off2, "fitness:", evaluation.fitness_8queen(off2))
offspring.append(off1)
offspring.append(off2)
def main():
experiment = [
# nothing yet
]
stat = ["mean", "std", "aes", "sr"]
evos = 0
while evos < 20:
trial = [-1, []] # [seccessed-generatiohn, best-fitness]
recorded = False
random.seed()
numpy.random.seed()
string_length = 8
popsize = 20 # original 20
mating_pool_size = int(popsize*0.5) # has to be even
tournament_size = 4
mut_rate = 0.2
xover_rate = 0.9
gen_limit = 50
# initialize population
gen = 0 # initialize the generation counter
population = initialization.permutation(popsize, string_length)
fitness = []
for i in range(0, popsize):
fitness.append(evaluation.fitness_8queen(population[i]))
print("generation", gen, ": best fitness", max(fitness), "average fitness", sum(fitness)/len(fitness))
# evolution begins
while gen < gen_limit:
# pick parents
#parents_index = parentSelection.MPS(fitness, mating_pool_size)
parents_index = parentSelection.tournament(fitness, mating_pool_size, tournament_size)
#parents_index = parentSelection.random_uniform(popsize, mating_pool_size)
# in order to randomly pair up parents
random.shuffle(parents_index)
# reproduction
offspring =[]
offspring_fitness = []
i= 0 # initialize the counter for parents in the mating pool
# offspring are generated using selected parents in the mating pool
while len(offspring) < mating_pool_size:
# recombination
if random.random() < xover_rate:
off1,off2 = recombination.permutation_cut_and_crossfill(population[parents_index[i]], population[parents_index[i+1]])
else:
off1 = population[parents_index[i]].copy()
off2 = population[parents_index[i+1]].copy()
# mutation
if random.random() < mut_rate:
off1 = mutation.permutation_swap(off1)
if random.random() < mut_rate:
off2 = mutation.permutation_swap(off2)
offspring.append(off1)
offspring_fitness.append(evaluation.fitness_8queen(off1))
offspring.append(off2)
offspring_fitness.append(evaluation.fitness_8queen(off2))
i = i+2 # update the counter
# form the population of next generation
population, fitness = survivorSelection.mu_plus_lambda(population, fitness, offspring, offspring_fitness)
# population, fitness = survivorSelection.replacement(population, fitness, offspring, offspring_fitness)
# population, fitness = survivorSelection.random_uniform(population, fitness, offspring, offspring_fitness)
gen = gen + 1 # update the generation counter
print("generation", gen, ": best fitness", max(fitness), "average fitness", sum(fitness)/len(fitness))
trial[1].append( max(fitness) )
#trial[1].append( sum(fitness)/len(fitness) )
if max(fitness) == 28 and recorded == False:
trial[0] = gen + 1
recorded = True
# evolution ends
# print the final best solution(s)
k = 0
for i in range (0, popsize):
if fitness[i] == max(fitness):
print("best solution", k, population[i], fitness[i])
k = k+1
evos = evos + 1
experiment.append(trial)
bests = []
sr = []
for trial in experiment:
final = trial[1][49]
bests.append(final)
if trial[0] != -1:
sr.append(trial[0])
stat[0] = numpy.mean(bests)
stat[1] = numpy.std(bests)
stat[3] = sr
stat[2] = numpy.mean(stat[3])
output_file = open("trace.js", "a")
output_file.write("let tourn_miu = ")
output_file.write(json.dumps(experiment))
output_file.write(";\n")
output_file.write("let tourn_miu_stat = ")
output_file.write(json.dumps(stat))
output_file.write(";\n")
output_file.close()
def main():
random.seed()
numpy.random.seed()
string_length = 8
popsize = 20
mating_pool_size = int(popsize * 0.5) # has to be even
tournament_size = 3
mut_rate = 0.2
xover_rate = 0.9
gen_limit = 50
# initialize population
gen = 0 # initialize the generation counter
population = initialization.permutation(popsize, string_length)
fitness = []
for i in range(0, popsize):
fitness.append(evaluation.fitness_8queen(population[i]))
print("generation", gen, ": best fitness", max(fitness), "average fitness",
sum(fitness) / len(fitness))
# evolution begins
while gen < gen_limit:
# pick parents
parents_index = parentSelection.MPS(fitness, mating_pool_size)
# parents_index = parentSelection.tournament(fitness, mating_pool_size, tournament_size)
# parents_index = parentSelection.random_uniform(popsize, mating_pool_size)
# in order to randomly pair up parents
random.shuffle(parents_index)
# reproduction
offspring = []
offspring_fitness = []
i = 0 # initialize the counter for parents in the mating pool
# offspring are generated using selected parents in the mating pool
while len(offspring) < mating_pool_size:
# recombination
if random.random() < xover_rate:
off1, off2 = recombination.permutation_cut_and_crossfill(
population[parents_index[i]],
population[parents_index[i + 1]])
else:
off1 = population[parents_index[i]].copy()
off2 = population[parents_index[i + 1]].copy()
# mutation
if random.random() < mut_rate:
off1 = mutation.permutation_swap(off1)
if random.random() < mut_rate:
off2 = mutation.permutation_swap(off2)
offspring.append(off1)
offspring_fitness.append(evaluation.fitness_8queen(off1))
offspring.append(off2)
offspring_fitness.append(evaluation.fitness_8queen(off2))
i = i + 2 # update the counter
# form the population of next generation
population, fitness = survivorSelection.mu_plus_lambda(
population, fitness, offspring, offspring_fitness)
# population, fitness = survivorSelection.replacement(population, fitness, offspring, offspring_fitness)
# population, fitness = survivorSelection.random_uniform(population, fitness, offspring, offspring_fitness)
gen = gen + 1 # update the generation counter
print("generation", gen, ": best fitness", max(fitness),
"average fitness",
sum(fitness) / len(fitness))
# evolution ends
# print the final best solution(s)
k = 0
for i in range(0, popsize):
if fitness[i] == max(fitness):
print("best solution", k, population[i], fitness[i])
k = k + 1
def main():
random.seed()
numpy.random.seed()
string_length = 8
popsize = 20
mating_pool_size = int(popsize * 0.5) # has to be even
tournament_size = 3
mut_rate = 0.2
xover_rate = 0.9
gen_limit = 100
# initialize population
cityList = []
c1 = City(2, 5, "c1")
c2 = City(4, 5, "c2")
c3 = City(9, 5, "c3")
c4 = City(12, 10, "c4")
c5 = City(20, 11, "c5")
c6 = City(13, 14, "c6")
c7 = City(20, 19, "c7")
c8 = City(21, 12, "c8")
c9 = City(210, 120, "c9")
c10 = City(165, 22, "c10")
c11 = City(35, 26, "c11")
c12 = City(50, 50, "c12")
c13 = City(205, 120, "c13")
c14 = City(154, 20, "c14")
c15 = City(150, 200, "c15")
c16 = City(130, 150, "c16")
c17 = City(135, 125, "c17")
c18 = City(110, 145, "c18")
c19 = City(25, 132, "c19")
c20 = City(60, 124, "c20")
cityList.append(c1)
cityList.append(c2)
cityList.append(c3)
cityList.append(c4)
cityList.append(c5)
cityList.append(c6)
cityList.append(c7)
cityList.append(c8)
cityList.append(c9)
cityList.append(c10)
cityList.append(c11)
cityList.append(c12)
cityList.append(c13)
cityList.append(c14)
cityList.append(c15)
cityList.append(c16)
cityList.append(c17)
cityList.append(c18)
cityList.append(c19)
cityList.append(c20)
gen = 0 # initialize the generation counter
population = initialization.permutation(popsize, cityList)
fitness = []
for i in range(0, popsize):
fitness.append(evaluation.fitness_8queen(population[i]))
print("generation", gen, ": best fitness", max(fitness), "average fitness",
sum(fitness) / len(fitness))
# evolution begins
while gen < gen_limit:
# pick parents
#parents_index = parentSelection.MPS(fitness, mating_pool_size)
parents_index = parentSelection.tournament(fitness, mating_pool_size,
tournament_size)
# parents_index = parentSelection.random_uniform(popsize, mating_pool_size)
# in order to randomly pair up parents
random.shuffle(parents_index)
# reproduction
offspring = []
offspring_fitness = []
i = 0 # initialize the counter for parents in the mating pool
# offspring are generated using selected parents in the mating pool
while len(offspring) < mating_pool_size:
# recombination
if random.random() < xover_rate:
off1, off2 = recombination.permutation_cut_and_crossfill(
population[parents_index[i]],
population[parents_index[i + 1]])
else:
off1 = population[parents_index[i]].copy()
off2 = population[parents_index[i + 1]].copy()
# mutation
if random.random() < mut_rate:
off1 = mutation.permutation_swap(off1)
if random.random() < mut_rate:
off2 = mutation.permutation_swap(off2)
offspring.append(off1)
offspring_fitness.append(evaluation.fitness_8queen(off1))
offspring.append(off2)
offspring_fitness.append(evaluation.fitness_8queen(off2))
i = i + 2 # update the counter
# form the population of next generation
population, fitness = survivorSelection.mu_plus_lambda(
population, fitness, offspring, offspring_fitness)
# population, fitness = survivorSelection.replacement(population, fitness, offspring, offspring_fitness)
# population, fitness = survivorSelection.random_uniform(population, fitness, offspring, offspring_fitness)
gen = gen + 1 # update the generation counter
print("generation", gen, ": best fitness", max(fitness),
"average fitness",
sum(fitness) / len(fitness))
# evolution ends
# print the final best solution(s)
k = 0
for i in range(0, popsize):
if fitness[i] == max(fitness):
print("best solution", k)
for j in range(0, len(population[i])):
print("city : ", population[i][j].name, "position : (",
population[i][j].x, ",", population[i][j].y, ")")
print("fitness :", fitness[i])
k = k + 1