def main():
global fileName_input, N
fileName_input = "fricker.txt"
fileName_output = fileName_input[:-4] + "_solutie.txt"
N = None
N, cityList=read()
# N, cityList=readCiudat()
print(cityList)
popSize = 100
noGen = 1000
mutationRate = 0.01
eliteSize = 20
ga = GA(popSize, eliteSize, cityList, mutationRate)
ga.initialise()
ga.evaluate()
maximFitness = -1
f = open(fileName_output, 'w')
global currentChromosome
for g in range(noGen):
currentChromosome = ga.bestChromosome()
if currentChromosome.fitness > maximFitness:
maximFitness = currentChromosome.fitness
# bestChromo = currentChromosome
f.write('Best solution in generation ' + str(g) + ' is: x = ' + str(currentChromosome.repres) + ' f(x) = ' + str(currentChromosome.fitness))
f.write('\n')
ga.oneGeneration()
f.write(str(N))
f.write('\n')
index = 0
for i in range(0, len(currentChromosome.repres)):
if currentChromosome.repres[i] == 0:
index = i
i = index
f.write(str(currentChromosome.repres[i] + 1) + ' ')
i += 1
while i != index:
if i == len(currentChromosome.repres):
i = 0
else:
f.write(str(currentChromosome.repres[i] + 1) + ' ')
i += 1
f.write('\n')
route = currentChromosome.repres
distance = 0
for i in range(0, len(route)):
fromCity = route[i]
toCity = None
if i + 1 < len(route):
toCity = route[i + 1]
else:
toCity = route[0]
distance += cityList[fromCity][toCity]
f.write(str(distance))
f.write('\n')
f.close()
fitness_avg = list()
fitness_max = list()
diversity = list()
valid_candidate = list()
gen = GA(population_cnt=_population_cnt,
rate_crossover=_rate_crossover,
rate_mutation=_rate_mutation,
sample_candidate=_sample_candidate)
print("Cross: " + str(_rate_crossover) + "- Mutation: " + str(_rate_mutation) +
"- tourSize: " + str(_tournament_size) + "- tourRate: " +
str(_tournament_win_rate))
for generation in range(_cnt_generations):
gen.mutate()
gen.crossover(strategy=_strategy)
gen.evaluate(calc_diversity=_calc_diversity)
gen.selection(tournament_win_rate=_tournament_win_rate,
tournament_size=_tournament_size)
if gen.generation % 10 == 0:
print("Gen: " + str(gen.generation) + "- Fitness_avg: " +
str(gen.fitness_avg) + "- Fitness_best: " +
str(gen.best_candidate.get_fitness()) + "- Diversity: " +
str(gen.diversity))
fitness_avg.append(gen.fitness_avg)
fitness_max.append(gen.best_candidate.get_fitness())
diversity.append(gen.diversity)
if (gen.best_candidate.get_fitness() > 1.0):
valid_candidate.append(deepcopy(gen.best_candidate))
for best in valid_candidate:
print("Candidate Fitness " + str(best.get_fitness()))
best.print()
