def __init__(self, max_dist, pop_size, reproducing_frac):
self.algs = []
self.all_sols = [] # all solutions, currently unused
self.gen_best = [] # best in the generation
self.done_iterations = 0
self.dist_m = DIST_M
self.df = DF
self.num_bikes = NUM_BIKES
self.max_dist = max_dist
self.pop_size = pop_size
self.num_reproducing = int(pop_size * reproducing_frac)
while len(self.algs
) < self.pop_size: #Initializing the population of GAs
new_alg = GA.Algorithm(parents=False, i=0)
if new_alg.get_distance(
self.dist_m) <= self.max_dist: # feasible solution
self.algs.append(new_alg)
def next_generation(self, i):
pot_par = self.algs[:]
self.infeasible = 0 # count infeasible solutions over simulation
num_par = len(pot_par)
repr_prob = sorted(list(range(0, num_par)), reverse=True)
repr_prob_sum = sum(repr_prob)
repr_prob = [r / repr_prob_sum for r in repr_prob]
while len(self.algs) < self.pop_size:
first_par = np.random.choice(pot_par, p=repr_prob)
second_par = np.random.choice(pot_par, p=repr_prob)
while first_par == second_par:
second_par = np.random.choice(pot_par, p=repr_prob)
new_alg = GA.Algorithm(parents=[first_par, second_par], i=i)
if new_alg.get_distance(
self.dist_m) <= self.max_dist: # feasible solution
self.algs.append(new_alg)
else:
self.infeasible += 1
