def _select(self, pop):
node = Node(self.problem, pop, self.settings.pop_size).divide(sqrt(pop))
non_dom_leafs = node.nonpruned_leaves()
all_leafs = node.leaves()
# Counting number of evals
evals = 0
for leaf in all_leafs:
for row in leaf._pop:
if row.evaluated:
evals+=1
return non_dom_leafs, evals
def _select(self, pop):
node = Node(self.problem, pop,
self.settings.pop_size).divide(sqrt(pop))
non_dom_leafs = node.nonpruned_leaves()
all_leafs = node.leaves()
# Counting number of evals
evals = 0
for leaf in all_leafs:
for row in leaf._pop:
if row.evaluated:
evals += 1
return non_dom_leafs, evals
def run(self, init_pop=None):
if init_pop is None:
init_pop = self.problem.populate(self.settings.pop_size)
population = Node.format(init_pop)
best_solutions = []
gen = 0
while gen < self.settings.gens:
say(".")
total_evals = 0
# SELECTION
selectees, evals = self.select(population)
solutions, evals = self.get_best(selectees)
best_solutions += solutions
total_evals += evals
# EVOLUTION
selectees, evals = self.evolve(selectees)
total_evals += evals
population, evals = self.recombine(selectees,
self.settings.pop_size)
total_evals += evals
gen += 1
print("")
return best_solutions
def initialize(self):
if not self.population:
self.population = self.problem.populate(self.settings.pop_size)
self.population = Node.format(self.population)
threshold = 2 * len(self.population) ** 0.5
leaves = Node(self.problem, self.population, len(self.population)).divide(threshold).leaves()
references = self.random_weights(len(leaves))
self.assign_references(leaves, references)
self.leaves = leaves
for leaf in leaves:
leaf.max_size = len(leaf.get_pop())
leaf.set_pop(get_quartile_range(leaf.get_pop()))
print(len(leaf.get_pop()))
return None
def run(self):
start = get_time()
if not self.population:
self.population = self.problem.populate(self.settings.pop_size)
population = Node.format(self.population)
best_solutions = []
while self.gen < self.settings.gens:
say(".")
self.gen += 1
# SELECTION
selectees = self.select(population)
solutions = self.get_best(selectees)
self.stat.update(solutions)
best_solutions += solutions
# EVOLUTION
selectees = self.evolve(selectees)
population = self.recombine(selectees, self.settings.pop_size)
self.stat.runtime = get_time() - start
return best_solutions
def run(self, init_pop=None):
if init_pop is None:
init_pop = self.problem.populate(self.settings.pop_size)
population = Node.format(init_pop)
best_solutions = []
gen = 0
while gen < self.settings.gens:
say(".")
total_evals = 0
# SELECTION
selectees, evals = self.select(population)
solutions, evals = self.get_best(selectees)
best_solutions += solutions
total_evals += evals
# EVOLUTION
selectees, evals = self.evolve(selectees)
total_evals += evals
population, evals = self.recombine(selectees, self.settings.pop_size)
total_evals += evals
gen += 1
print("")
return best_solutions
def _recombine(self, mutants, total_size):
remaining = total_size - len(mutants)
pop = []
for _ in range(remaining):
pop.append(self.problem.generate())
return mutants + Node.format(pop), 0
def _recombine(self, mutants, total_size):
remaining = total_size - len(mutants)
pop = []
for _ in range(remaining):
pop.append(self.problem.generate())
return mutants + Node.format(pop), 0
def _select(self, pop): node = Node(self.problem, pop, self.settings.pop_size).divide(sqrt(pop)) non_dom_leafs = node.nonpruned_leaves() return non_dom_leafs
