set_raw_score(a, -get_mixing_energy(a))
db.add_relaxed_step(a)
pop.update()
# Below is the iterative part of the algorithm
gen_num = db.get_generation_number()
for i in range(num_gens):
print('Creating and evaluating generation {0}'.format(gen_num + i))
new_generation = []
for _ in range(pop_size):
# Select parents for a new candidate
parents = pop.get_two_candidates()
# Select an operator and use it
op = operation_selector.get_operator()
offspring, desc = op.get_new_individual(parents)
# An operator could return None if an offspring cannot be formed
# by the chosen parents
if offspring is None:
continue
set_raw_score(offspring, -get_mixing_energy(offspring))
new_generation.append(offspring)
# We add a full relaxed generation at once, this is faster than adding
# one at a time
db.add_more_relaxed_candidates(new_generation)
# update the population to allow new candidates to enter
pop.update()
