def comp_by_cv_and_fitness(pop, P, **kwargs):
S = np.full(P.shape[0], np.nan)
for i in range(P.shape[0]):
a, b = P[i, 0], P[i, 1]
# if at least one solution is infeasible
if pop[a].CV > 0.0 or pop[b].CV > 0.0:
S[i] = compare(a,
pop[a].CV,
b,
pop[b].CV,
method='smaller_is_better',
return_random_if_equal=True)
# both solutions are feasible just set random
else:
S[i] = compare(a,
pop[a].F,
b,
pop[b].F,
method='smaller_is_better',
return_random_if_equal=True)
return S[:, None].astype(int)
def binary_tournament(pop, P, algorithm, **kwargs):
n_tournaments, n_parents = P.shape
if n_parents != 2:
raise ValueError("Only implemented for binary tournament!")
tournament_type = algorithm.tournament_type
S = np.full(n_tournaments, np.nan)
for i in range(n_tournaments):
a, b = P[i, 0], P[i, 1]
a_cv, a_f, b_cv, b_f, = pop[a].CV[0], pop[a].F, pop[b].CV[0], pop[b].F
rank_a, cd_a = pop[a].get("rank", "crowding")
rank_b, cd_b = pop[b].get("rank", "crowding")
# if at least one solution is infeasible
if a_cv > 0.0 or b_cv > 0.0:
S[i] = compare(a,
a_cv,
b,
b_cv,
method='smaller_is_better',
return_random_if_equal=True)
# both solutions are feasible
else:
if tournament_type == 'comp_by_dom_and_crowding':
rel = Dominator.get_relation(a_f, b_f)
if rel == 1:
S[i] = a
elif rel == -1:
S[i] = b
elif tournament_type == 'comp_by_rank_and_crowding':
S[i] = compare(a,
rank_a,
b,
rank_b,
method='smaller_is_better')
else:
raise Exception("Unknown tournament type.")
# if rank or domination relation didn't make a decision compare by crowding
if np.isnan(S[i]):
S[i] = compare(a,
cd_a,
b,
cd_b,
method='larger_is_better',
return_random_if_equal=True)
return S[:, None].astype(int, copy=False)
def constr_binary_tournament(pop, P, algorithm, **kwargs):
n_tournaments, n_parents = P.shape
if n_parents != 2:
raise ValueError("Only implemented for binary tournament!")
S = np.full(n_tournaments, np.nan)
for i in range(n_tournaments):
a, b = P[i, 0], P[i, 1]
a_cv, a_f, b_cv, b_f, = pop[a].CV[0], pop[a].F, pop[b].CV[0], pop[b].F
a_below, b_below = pop[a].get("below"), pop[b].get("below")
if a_cv > 0.0 and b_cv > 0.0:
if a_below and b_below:
S[i] = compare(a,
a_cv,
b,
b_cv,
method='smaller_is_better',
return_random_if_equal=True)
elif not a_below and not b_below:
S[i] = compare(a,
a_f,
b,
b_f,
method='smaller_is_better',
return_random_if_equal=True)
elif a_below and not b_below:
S[i] = a
elif not a_below and b_below:
S[i] = b
elif a_cv == 0.0 and b_cv == 0.0:
S[i] = np.random.choice([a, b])
# S[i] = compare(a, a_f, b, b_f, method='smaller_is_better', return_random_if_equal=True)
else:
S[i] = np.random.choice([a, b])
return S[:, None].astype(int, copy=False)
def comp_by_cv_and_clearing_fitness(pop, P, **kwargs):
S = np.full(P.shape[0], np.nan)
for i in range(P.shape[0]):
a, b = P[i, 0], P[i, 1]
# if at least one solution is infeasible
if pop[a].CV[0] > 0.0 or pop[b].CV[0] > 0.0:
S[i] = compare(a, pop[a].CV, b, pop[b].CV,
method='smaller_is_better',
return_random_if_equal=True)
# first compare by the round the individual was selected
else:
S[i] = compare(a, pop[a].get("iter"), b, pop[b].get("iter"), method='smaller_is_better')
# if it was the same round - then use the rank of the fitness directly
if np.isnan(S[i]):
S[i] = compare(a, pop[a].get("rank"), b, pop[b].get("rank"),
method='smaller_is_better', return_random_if_equal=True)
return S[:, None].astype(int)
def comp_by_rank_and_ref_line_dist(pop, P, **kwargs):
S = np.full(P.shape[0], np.nan)
for i in range(P.shape[0]):
a, b = P[i, 0], P[i, 1]
# if at least one solution is infeasible
if pop[a].CV > 0.0 or pop[b].CV > 0.0:
S[i] = compare(a,
pop[a].CV,
b,
pop[b].CV,
method='smaller_is_better',
return_random_if_equal=True)
# both solutions are feasible
else:
# if in the same niche select by rank
if pop[a].get("niche") == pop[b].get("niche"):
if pop[a].get("rank") != pop[b].get("rank"):
S[i] = compare(a,
pop[a].get("rank"),
b,
pop[b].get("rank"),
method='smaller_is_better')
else:
S[i] = compare(a,
pop[a].get("dist_to_niche"),
b,
pop[b].get("dist_to_niche"),
method='smaller_is_better')
if np.isnan(S[i]):
S[i] = np.random.choice([a, b])
return S[:, None].astype(int)