def main(phenotype_class):
popsize = int(input("Input population size: "))
babymaker = production.production(phenotype_class)
generations = int(input("Input number of generations: "))
goal = float(input("Input fitness goal, 0 if not applicable: "))
protocol = adult_selection.protocols[raw_input("Input the wanted selection protocol (" + "/".join(adult_selection.protocols.keys()) + "): ")](popsize)
mechanism = selection_mechanisms.mechanisms[raw_input("Input the wanted selection mechanism (" + "/".join(selection_mechanisms.mechanisms.keys()) + "): ")](protocol.litter_size)
pop = population.population([phenotype_class() for i in range(popsize)])
population_list = [pop]
stdout.write("\nProgress: generation ")
generation = 0
generation_s = ""
while (goal == 0 and generation <= generations) or (goal != 0 and population_list[-1].max_fitness() < goal and generation <= generations):
parents = mechanism.select(pop)
litter = babymaker.produce(parents)
pop = protocol.select(pop, litter)
population_list += [deepcopy(pop)]
stdout.write("\b"*len(generation_s))
generation += 1
generation_s = str(generation)
stdout.write(generation_s)
stdout.write("\nDone!\n")
return population_list
def produce(this, parents): pairs = [(parents[i], parents[i+1]) for i in xrange(0, len(parents), 2)] litter = [] for mom, dad in pairs: child1, child2 = mom.crossover(dad, this.crossover_points, this.crossover_rate) this.mutation_type(child1, this.mutation_rate) this.mutation_type(child2, this.mutation_rate) litter += [this.ptype_class(gene=child1.gene), this.ptype_class(gene=child2.gene)] return population.population(litter)
def populate(data):
ranges = load_data(data)
p = population.population(9, ranges)
for i in range(0, 20):
for c in p.colony:
c.fitness_score = c.cal_fitness(data)
p.next_generation()
p.keep_size()
return p.best_gene()
traits=parameter.__getstate__()
real_trans=parameter.real_trans
m=parameter.m #每期重复的轮次
n=parameter.n #期数
resource_rate=parameter.resource_rate #拥有资源的概率
imitate_rate=parameter.imitate_rate #变异率
c=parameter.battle_cost #战斗成本
evo_pop=population([parameter.number_of_B,parameter.number_of_X,parameter.number_of_E]) #设定B、X、E三者的人数
endowment_effect=parameter.endowment_effect #禀赋效应
pro_func=eval("lambda x:"+parameter.profit_function)
w=parameter.selection_strength #选择强度
class evolution(object):
def __init__(self,evo_pop,pro_func,endowment_effect,resource_rate,battle_cost,imitate_rate,selection_strength,m,n):
self.evo_pop=evo_pop
self.profit_func=pro_func
self.endowment_effect=endowment_effect
self.resource_rate=resource_rate
self.battle_cost=battle_cost
self.imitate_rate=imitate_rate
# create the new dir
import os
## os.mkdir(dirName) # didnt work
try:
os.makedirs(dirName) # so lets try
# make a copy of the origenal gene
originalGene.save(dirName+os.sep+'originalGene.model')
except:
print('what went rong?')
print(sys.exc_type)
print(sys.exc_value)
print(sys.exc_traceback)
# start a new population
pop=population()
# set GA settings
pop.maxTime=timedelta(hours=60)
# create a population using the orignial gene
pop.seed(originalGene)
pop.evolve()
#
# make a models dir
## os.mkdir(dirName+'models') # sometimes does not work on linux for some reason
os.makedirs(dirName+os.sep+'models'+str(uniqueNumber))
for i in range(len(pop.population)):
pop.population[i].save(dirName+os.sep+'models'+str(uniqueNumber)+os.sep+'model_'+str(i)+'.model')
# save history
pop.saveProgress(dirName+os.sep+'log'+str(uniqueNumber)+'.txt')
def test_generator_creates_enough_members():
p = population(100, lambda: randint(0, 100))
assert (len(p.pop) == 100)
def get_random_population(size):
return population(size, random_bitarray_chromosome)
from population import population
from chromosome import chromosome
import random
noOfGen = 25
popDim = 25
pop = population(popDim)
pop.initPop()
f = open("out.txt", "w")
bestArr = []
for i in range(noOfGen):
popAux = []
pop.fitPop()
best = pop.getBest()
bestArr.append(best.getVelocity())
print(best.getFitness())
for j in range(popDim):
c1 = pop.selection()
c2 = pop.selection()
c = pop.xo(c1, c2)
if random.uniform(0, 1) < 0.15:
c.mutation()
popAux.append(c)