def createCSV(arg):
pp("Creating CSV file...")
length = len(arg)
csv = open('solution_{0}.csv'.format(str(datetime.datetime.now().time())[:-7]), 'w')
for i in range(length):
data = str(arg[i])
csv.write(data+"\n")
pp("Created CSV file")
return csv
def updateFitness(parents, cities):
pp("Updating fitness of the mutated...")
for parent in parents:
fit = computeFitness(parent.getList(), cities)
parent.setFitness(fit)
pp("Updated fitness of the mutated")
return parents
def mutate(crossovered, r):
pp("Mutating...")
for parent in crossovered:
li = parent.getList()
mutated_li = mutateIndividual(li, r)
parent.setList(mutated_li)
pp("Mutated")
return crossovered
def initialPopulation(num, pop, cities):
pp("Generating intial population...")
population = [0 for x in range(pop)]
alignedList = list(range(1, num+1))
# generate random permutated lists
count = 0
while count < pop:
l = copy.copy(alignedList)
random.shuffle(l) # not aligned anymore
route = l
fitness = computeFitness(route, cities)
population[count] = Parent(route, fitness)
count += 1
pp("Generated intial population")
return population
def sampleSUS(parents, N):
pp("Sampling using SUS...")
selected = [0 for x in range(N)] #[0, 0, ..., 0]
cumul_prob = getCumulProb(parents)
current_member = 0
i = 0
r = random.uniform(0, 1/N)
while(current_member <= N-1):
while(r <= cumul_prob[i]):
selected[current_member] = parents[i]
r += 1/N
current_member += 1
i += 1
pp("Sampled using SUS")
return selected
def chooseBestGeneration(parent, child, m):
pp("Choosing the best generation...")
best = []
l = len(parent)
numElite = int(l*m)
#numElite = 2
aligned_parent = alignFitness(parent)
best_parent = aligned_parent[:numElite]
aligned_child = alignFitness(child)
best_child = aligned_child[:-numElite]
best = best_parent + best_child
pp("Chose the best generation")
return best
def orderedCrossover(selected, r, pop, cities):
pp("Crossovering...")
childs = []
numChild = 0
numPop = int(0.5*pop if pop%2==0 else 0.5*pop+1)
while numChild < numPop:
pairs = makePair(selected)
for pair in pairs:
numChild += 1
pp("Creating Child #{0}...".format(numChild))
if len(pair) == 2:
child = breed(pair[0], pair[1], r, cities)
else:
child = pair[0]
childs.append(child)
pp("Crossovered")
return childs
def chooseBestOne(pop):
pp("Choosing the best one...")
sorted_pop = alignFitness(pop)
pp("Chose the best one")
return sorted_pop[0]
