def reproducePopulation(population, numberOfReproductions, mutationProbability,
gammaK, gammaTheta):
populationSize = len(population)
while numberOfReproductions > 0:
# escolher pais utilizando distribuicao gamma
fatherIndex = int(gammavariate(gammaK, gammaTheta)) % populationSize
motherIndex = fatherIndex
while motherIndex == fatherIndex:
motherIndex = int(gammavariate(gammaK,
gammaTheta)) % populationSize
father = population[fatherIndex]
mother = population[fatherIndex]
# determinar qual o pai mais forte
if father.value < mother.value:
stronger = father
weaker = mother
else:
stronger = mother
weaker = father
# quanto mais forte o pai mais cromossomos passa, pais fracos (valor > 8) passam em media metade dos cromossomos
separationRandomization = gammavariate(stronger.value % 8 + 1, 1 / 2)
separationIndex = int(round(separationRandomization)) % 4 + 1
# chance de swap de maneira que ambos extremos dos cromossomos do mais forte possam ser passados
if random() < 0.5:
stronger, weaker = weaker, stronger
separationIndex = 8 - separationIndex
# gera filho
child = State()
i = 0
while i < separationIndex:
child.queen[i] = stronger.queen[i]
i += 1
while i < 8:
child.queen[i] = weaker.queen[i]
i += 1
# mutacao
if random() < mutationProbability:
child.queen[randrange(8)] = randrange(8)
child.evaluate()
# filho eh inserido na populacao no lugar de alguem escolhido de acordo com uma distribuicao gamma
replaceIndex = populationSize - int(gammavariate(
gammaK, gammaTheta)) % populationSize - 1
#print(populationSize, replaceIndex) #debug
population.pop(replaceIndex)
population.insert(0, child)
numberOfReproductions -= 1
# Recommended values: <initial temperature> = 10-1000, <cooling rate> = 0.005-0.01 # python3 main.py GENETIC_ALGORITHM <population size> <reproduction rate> <mutation probability> <gamma k> <gamma theta> # Recommended values: <population size> = 1000, <reproduction rate> = 0.5, <mutation probability> = 0.15, <gamma k> = 2, <gamma theta> = <population size>/(4 x <gamma k>) # The values of k and theta determine who will be selected for reproduction and substitution. The recomended values will make so that, in general, the 25% best will reproduce and the 25% worst will be replaced (Average = k x theta). Smaller values of k increase the elitism, while smaller values get the distribution closer to a normal distribution # # # The file contains 8 integers where each represent the colum the queen in that line is # # In case it's not possible to find a solution with the given initial state, the state is shuffled and the choses algorithm runs again using the shuffled state. solution = State() tries = 0 if sys.argv[2] == 'HILL_CLIMBING': solution.readFromFile( open(sys.argv[1], 'r') ) solution.evaluate() sidewayLimit = int(sys.argv[3]) while solution.value > 0: tries += 1 solution = hillClimb(solution, sidewayLimit) if solution.value > 0: solution.randomize() solution.evaluate() elif sys.argv[2] == 'SIMULATED_ANNEALING': solution.readFromFile( open(sys.argv[1], 'r') ) solution.evaluate() temperature = float(sys.argv[3]) coolingRate = float(sys.argv[4])