def evolve_same_pop(self, iteration):
"""Step algorithm using 'Sampe population' method."""
bestTwo = self.get_best_tours(self.tours)
worstTwo = self.get_worst_tours(self.tours)
children1, children2 = self.crossover(self.tours[bestTwo[0][0]].cities,
self.tours[bestTwo[1][0]].cities)
child1 = Tour(self.lx, self.ly)
child2 = Tour(self.lx, self.ly)
child1.set_tour(children1)
child2.set_tour(children2)
if self.tours[worstTwo[0][0]].get_cost() > child1.get_cost():
self.tours[worstTwo[0][0]] = child1
if self.tours[worstTwo[1][0]].get_cost() > child2.get_cost():
self.tours[worstTwo[1][0]] = child2
for i in range(len(self.tours)):
self.tours[i] = self.mutate(self.tours[i])
def evolve_new_pop(self, iteration):
"""Step algorithm using 'New population' method."""
new_tours = []
# Save best tour
_, best_tuple = self.get_best_tours(self.tours)
best = self.tours[best_tuple[0]]
new_tours.append(best)
for i in range(1, len(self.tours)):
parent1 = self.tournament_selection()
parent2 = self.tournament_selection()
child1, child2 = self.crossover(parent1.cities, parent2.cities)
t = Tour(self.lx, self.ly)
t.set_tour(child1)
new_tours.append(t)
self.tours = new_tours
for i in range(1, len(self.tours)):
self.tours[i] = self.mutate(self.tours[i])
_, best_tuple = self.get_best_tours(self.tours)
best = self.tours[best_tuple[0]]