xkuzz / pyade Goto Github PK

Hello,
Is it possible to add more inputs that are fixed parameters needed to completely specify the objective function (different to parameter need to estimate)?
Thank you very much for sharing your library. It helps me so much.

Hi, I am reading your code on the Lshade algorithm. First of all, it is very well written, congrats. Second the variable archive doesn't seems to do anything, am I wrong or there is a function that select randomly from population U archive?

The function init_population(...) uses np.random.randn() and forces the values to stay within the limits. But this does not sample the entire search space bounded by the bounds. My suggestion is a function like this:

def init_population(population_size: int, individual_size: int,
bounds: Union[np.ndarray, list]) -> np.ndarray:
population = []
for _ in range(population_size):
x = []
for i in range(individual_size):
xi = np.random.rand() * (bounds[i][1]-bounds[i][0]) + bounds[i][0]
gen.append(xi)
population.append(x)
population = np.array(population)
return population