def optimize_route(objects, generations, crossover_chance, mutation_chance,
population):
"""
Return list of points ordered by genetic algorithm.
"""
generator = RouteGenerator(objects)
ga = GeneticAlgorithm(
generator, crossover_chance, mutation_chance, population
)
print '\nINITIAL ROUTES'
print ', '.join([
str(int(route.get_length())) for route in ga.get_solutions()
])
# initial = [route.get_length() for route in ga.get_solutions()]
# best_initial = min(initial)
# while ga.get_best_solution().get_length() > best_initial * 0.5:
# # step condition instead of generations number
# # print ga.get_best_solution().get_length(), best_initial
# ga.evolve()
for _ in range(generations):
ga.evolve()
print '\nLAST GENERATION'
print ', '.join([
str(int(route.get_length())) for route in ga.get_solutions()
])
best = ga.get_best_solution()
print '\nLENGTH OF ROUTE AFTER OPTIMIZATION'
print int(best.get_length())
return best.objects
def optimize_route(objects, generations, crossover_chance, mutation_chance,
population):
"""
Return list of points ordered by genetic algorithm.
"""
generator = RouteGenerator(objects)
ga = GeneticAlgorithm(generator, crossover_chance, mutation_chance,
population)
print '\nINITIAL ROUTES'
print ', '.join(
[str(int(route.get_length())) for route in ga.get_solutions()])
# initial = [route.get_length() for route in ga.get_solutions()]
# best_initial = min(initial)
# while ga.get_best_solution().get_length() > best_initial * 0.5:
# # step condition instead of generations number
# # print ga.get_best_solution().get_length(), best_initial
# ga.evolve()
for _ in range(generations):
ga.evolve()
print '\nLAST GENERATION'
print ', '.join(
[str(int(route.get_length())) for route in ga.get_solutions()])
best = ga.get_best_solution()
print '\nLENGTH OF ROUTE AFTER OPTIMIZATION'
print int(best.get_length())
return best.objects
def main():
# Define objective class
objective_class = Objective_function(func1, 2, lo_bounds1, up_bounds1)
#set PSO optimizer
ga = GeneticAlgorithm(nb_generations=20,
nb_populations=10,
objective_class=objective_class)
ga.evolve()
for i in ga.populations:
print(i.fitness)