def aging(self, task, pop):
r"""Apply aging to individuals.
Args:
task (Task): Optimization task.
pop (numpy.ndarray[Individual]): Current population.
Returns:
numpy.ndarray[Individual]: New population.
"""
fpop = np.asarray([x.f for x in pop])
x_b, x_w = pop[np.argmin(fpop)], pop[np.argmax(fpop)]
avg, npop = np.mean(fpop[fpop != np.inf]), []
for x in pop:
x.age += 1
Lt = round(
self.age(Lt_min=self.Lt_min,
Lt_max=self.Lt_max,
mu=self.mu,
x_f=x.f,
avg=avg,
x_gw=x_w.f,
x_gb=x_b.f))
if x.age <= Lt: npop.append(x)
if len(npop) == 0:
npop = objects2array([
self.itype(task=task, rnd=self.Rand, e=True)
for _ in range(self.NP)
])
return npop
def pop_increment(self, pop, task):
r"""Increment population.
Args:
pop (numpy.ndarray): Current population.
task (Task): Optimization task.
Returns:
numpy.ndarray: Increased population.
"""
deltapop = int(round(max(1, self.NP * self.delta_pop_e(task.Iters))))
return objects2array([
self.itype(task=task, rnd=self.Rand, e=True)
for _ in range(deltapop)
])
def evolve(self, pop, xb, task, **kwargs):
r"""Evolve population with the help multiple mutation strategies.
Args:
pop (numpy.ndarray): Current population.
xb (numpy.ndarray): Current best individual.
task (Task): Optimization task.
kwargs (Dict[str, Any]): Additional arguments.
Returns:
numpy.ndarray: New population of individuals.
"""
return objects2array([
self.CrossMutt(pop, i, xb, self.F, self.CR, self.Rand, task,
self.itype, self.strategies)
for i in range(len(pop))
])
def pop_decrement(self, pop, task):
r"""Decrement population.
Args:
pop (numpy.ndarray): Current population.
task (Task): Optimization task.
Returns:
numpy.ndarray: Decreased population.
"""
deltapop = int(round(max(1, self.NP * self.delta_pop_c(task.Iters))))
if len(pop) - deltapop <= 0: return pop
ni = self.Rand.choice(len(pop), deltapop, replace=False)
npop = []
for i, e in enumerate(pop):
if i not in ni: npop.append(e)
elif self.rand() >= self.omega: npop.append(e)
return objects2array(npop)
def evolve(self, pop, xb, task, **ukwargs):
r"""Evolve current population.
Args:
pop (numpy.ndarray[Individual]): Current population.
xb (Individual): Global best individual.
task (Task): Optimization task.
ukwargs (Dict[str, Any]): Additional arguments.
Returns:
numpy.ndarray: New population.
"""
npop = objects2array([self.AdaptiveGen(e) for e in pop])
for i, e in enumerate(npop):
npop[i].x = self.CrossMutt(npop, i, xb, e.F, e.CR, rnd=self.Rand)
for e in npop:
e.evaluate(task, rnd=self.rand)
return npop
def init_pop_individual(task, n, itype, **kwargs):
r"""Custom population initialization function for numpy individual type.
Args:
task (Task): Optimization task.
n (int): Population size.
itype (Individual): Type of individual in population.
kwargs (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, list, dict):
1. Initialized population.
2. Initialized populations fitness/function values.
3. Additional arguments.
4. Additional keyword arguments.
"""
pop = objects2array(
[itype(x=np.full(task.D, 0.0), task=task) for _ in range(n)])
return pop, np.asarray([x.f for x in pop]), [], {}
def evolve(self, pop, xb, task, **kwargs):
r"""Evolve population.
Arg:
pop (numpy.ndarray): Current population.
xb (Individual): Current best individual.
task (Task): Optimization task.
kwargs (Dict[str, Any]): Additional arguments.
Returns:
numpy.ndarray: New evolved populations.
"""
return objects2array([
self.itype(x=self.CrossMutt(pop, i, xb, self.F, self.CR,
self.Rand),
task=task,
rnd=self.Rand,
e=True) for i in range(len(pop))
])
def selection(self, pop, npop, xb, fxb, task, **kwargs):
r"""Operator for selection.
Args:
pop (numpy.ndarray): Current population.
npop (numpy.ndarray): New Population.
xb (numpy.ndarray): Current global best solution.
fxb (float): Current global best solutions fitness/objective value.
task (Task): Optimization task.
kwargs (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, float]:
1. New selected individuals.
2. New global best solution.
3. New global best solutions fitness/objective value.
"""
arr = objects2array(
[e if e.f < pop[i].f else pop[i] for i, e in enumerate(npop)])
xb, fxb = self.get_best(arr, np.asarray([e.f for e in arr]), xb, fxb)
return arr, xb, fxb
def post_selection(self, pop, task, xb, fxb, **kwargs):
r"""Post selection operator.
In this algorithm the post selection operator decrements the population at specific iterations/generations.
Args:
pop (numpy.ndarray): Current population.
task (Task): Optimization task.
kwargs (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, float]:
1. Changed current population.
2. New global best solution.
3. New global best solutions fitness/objective value.
"""
Gr = task.nFES // (self.pmax * len(pop)) + self.rp
nNP = len(pop) // 2
if task.Iters == Gr and len(pop) > 3:
pop = objects2array([
pop[i] if pop[i].f < pop[i + nNP].f else pop[i + nNP]
for i in range(nNP)
])
return pop, xb, fxb