本文整理汇总了Python中Population.Population.get_fittest方法的典型用法代码示例。如果您正苦于以下问题：Python Population.get_fittest方法的具体用法？Python Population.get_fittest怎么用？Python Population.get_fittest使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Population.Population的用法示例。
在下文中一共展示了Population.get_fittest方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from Population import Population [as 别名]
# 或者: from Population.Population import get_fittest [as 别名]
def main():
#Get the sequence to be searched using
FitnessCalculator.set_solution(str(raw_input("SEQUENCE: ")))
#Define the population
pop_size = int(raw_input("Enter population size: "))
elite_option = str(raw_input("Elitism enabled?(y/n): "))
#Elite switch
if elite_option == 'y':
elitism_enabled = True
else:
elitism_enabled = False
#Create a starting popultation
population = Population(pop_size, True)
#Generation count
generation = 0
evolution = Evolution(elitism_enabled)
#Loop until we dont have a solution
while population.get_fittest().get_fitness() < FitnessCalculator.get_max_fitness():
generation += 1
print "GENERATION : " + str(generation) + " FITTEST: " + str(population.get_fittest().get_fitness())
#Evolve the population
population = evolution.evolve(population)
print "SOLUTION FOUND..."
print "GENERATION: " + str(generation)
print "GENES: " + population.get_fittest().get_string()
return示例2: tournament_selection
# 需要导入模块: from Population import Population [as 别名]
# 或者: from Population.Population import get_fittest [as 别名]
def tournament_selection(self, population):
# Create a new tournament population
tournament_population = Population(self.tournament_size, False)
for i in range(0, self.tournament_size):
# Select a random individual from the pool for breeding qualification
index = random.randint(0, population.get_size() - 1)
tournament_population.save_individual(i, population.get_individual(index))
# Return the fittest individual from the tournament for breeding
return tournament_population.get_fittest()示例3: tournament_selection
# 需要导入模块: from Population import Population [as 别名]
# 或者: from Population.Population import get_fittest [as 别名]
def tournament_selection(population_passed):
#Tournament pool
tournament = Population(Algorithm.Tournament_size, False)
""" Tournament selection technique.
How it works: The algorithm choose randomly five
individuals from the population and returns the fittest one """
for i in range(Algorithm.Tournament_size):
random_id = int(random() * population_passed.size())
tournament.individuals.append(population_passed.get_individual(random_id))
fittest = tournament.get_fittest()
return fittest示例4: time
# 需要导入模块: from Population import Population [as 别名]
# 或者: from Population.Population import get_fittest [as 别名]
from FitnessCalc import FitnessCalc
from Population import Population
from Algorithm import Algorithm
#from time import time
start = time()
FitnessCalc.set_solution("1111000000000000000000000000000000000000000000000000000000001111")
my_pop = Population(50, True)
generation_count = 0
while my_pop.fitness_of_the_fittest() != FitnessCalc.get_max_fitness():
generation_count += 1
print("Generation : %s Fittest : %s " % (generation_count, my_pop.fitness_of_the_fittest()))
my_pop = Algorithm.evolve_population(my_pop)
print("******************************************************")
genes_the_fittest = []
for i in range(len(FitnessCalc.Solution)):
genes_the_fittest.append(my_pop.get_fittest().genes[i])
print("Solution found !\nGeneration : %s Fittest : %s " % (generation_count + 1, my_pop.fitness_of_the_fittest()))
print("Genes of the Fittest : %s " % (genes_the_fittest))
finish = time()
print ("Time elapsed : %s " % (finish - start))