def __init__(self, cromossom=30 * np.random.random(30) - 15, sigma=np.random.random(), it_number=1):
self.cromossom = cromossom
self.sigma = sigma
self.fitness = ackleyFunc(self.cromossom)
self.threshold = 0.5
self.eps = 1e-12
self.it_number = it_number
def mutate_index(self, i):
sigma = self.sigma[i] * np.exp(self.t * np.random.randn() + self.tl * np.random.randn())
sigma = np.maximum(sigma, self.threshold)
cromossom = self.cromossom
cromossom[i] = self.cromossom[i] + np.random.randn()*sigma
if ackleyFunc(cromossom) <= self.fitness:
self.cromossom[i] = cromossom[i]
self.sigma[i] = sigma
def __init__(self, cromossom=30 * np.random.random(30) - 15, sigma=np.random.random(), improvement_count=0,
it_number=1):
self.cromossom = cromossom
self.sigma = sigma
self.fitness = ackleyFunc(self.cromossom)
self.eps = 1e-12
self.improvement_count = improvement_count
self.it_number = it_number
self.sigma_parameter = 0.95
def __init__(self, cromossom=30 * np.random.random(30) - 15, sigma=np.random.random(30), it_number=1):
self.cromossom = cromossom
self.sigma = sigma
self.fitness = ackleyFunc(self.cromossom)
self.eps = 1e-12
self.it_number = it_number
self.sigma_parameter = 0.95
self.t = 0.05 / np.sqrt(2 * 30)
self.tl = 0.05 / np.sqrt(np.sqrt(2 * 30))
self.threshold = 1
def set_fitness(self):
self.fitness = ackleyFunc(self.cromossom)