variable_function=get_comp)
# Evaluate the starting population
# The only requirement of the evaluation is to set the raw_score
# Negative mixing energy means more stable than the pure slabs
# The optimization always progress towards larger raw score,
# so we take the negative mixing energy as the raw score
print('Evaluating initial candidates')
while db.get_number_of_unrelaxed_candidates() > 0:
a = db.get_an_unrelaxed_candidate()
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
