from abc import abstractmethod
import numpy as np
from pymoo.core.population import Population
from pymoo.util import default_random_state
# ---------------------------------------------------------------------------------------------------------
# Survival
# ---------------------------------------------------------------------------------------------------------
[docs]
class Survival:
def __init__(self, filter_infeasible=True):
super().__init__()
self.filter_infeasible = filter_infeasible
@default_random_state
def do(self,
problem,
pop,
*args,
n_survive=None,
random_state=None,
return_indices=False,
**kwargs):
# make sure the population has at least one individual
if len(pop) == 0:
return pop
if n_survive is None:
n_survive = len(pop)
n_survive = min(n_survive, len(pop))
# if the split should be done beforehand
if self.filter_infeasible and problem.has_constraints():
# split feasible and infeasible solutions
feas, infeas = split_by_feasibility(pop, sort_infeas_by_cv=True)
if len(feas) == 0:
survivors = Population()
else:
survivors = self._do(problem, pop[feas], *args, n_survive=min(len(feas), n_survive),
random_state=random_state, **kwargs)
# calculate how many individuals are still remaining to be filled up with infeasible ones
n_remaining = n_survive - len(survivors)
# if infeasible solutions needs to be added
if n_remaining > 0:
survivors = Population.merge(survivors, pop[infeas[:n_remaining]])
else:
survivors = self._do(problem, pop, *args, n_survive=n_survive, random_state=random_state, **kwargs)
if return_indices:
H = {}
for k, ind in enumerate(pop):
H[ind] = k
return [H[survivor] for survivor in survivors]
else:
return survivors
@abstractmethod
def _do(self, problem, pop, *args, n_survive=None, random_state=None, **kwargs):
pass
class ToReplacement(Survival):
def __init__(self, survival):
super().__init__(False)
self.survival = survival
def _do(self, problem, pop, off, random_state=None, **kwargs):
merged = Population.merge(pop, off)
I = self.survival.do(problem, merged, n_survive=len(merged), return_indices=True, random_state=random_state, **kwargs)
merged.set("__rank__", I)
for k in range(len(pop)):
if off[k].get("__rank__") < pop[k].get("__rank__"):
pop[k] = off[k]
return pop
def split_by_feasibility(pop, sort_infeas_by_cv=True, sort_feas_by_obj=False, return_pop=False):
F, CV, b = pop.get("F", "CV", "FEAS")
feasible = np.where(b)[0]
infeasible = np.where(~b)[0]
if sort_infeas_by_cv:
infeasible = infeasible[np.argsort(CV[infeasible, 0])]
if sort_feas_by_obj:
feasible = feasible[np.argsort(F[feasible, 0])]
if not return_pop:
return feasible, infeasible
else:
return feasible, infeasible, pop[feasible], pop[infeasible]
