我正在用python做遗传算法的实验。换句话说，我想做一个程序来模拟随机过程和控制的部分(如实验)来玩变量和结果。
对于我的程序，我正在模拟老鼠的繁殖并控制其数量，以最终创造出更优质(在我的情况下，更重)的老鼠。具体来说，我想要有一个起始种群的大鼠，随机配对两只进行繁殖(后代的体重将达到两个亲本大鼠的平均体重)，并控制后代的数量，等等。简单来说，我将有x种群的老鼠(初始和恒定)，创造后代，按重量列出它们，从底部杀死(最轻的老鼠)以降低后代种群==初始种群，重复该过程，最终创造出更重的老鼠。
我知道这个实验不能反映现实生活中的例子，但我想用这个程序进行初始化，并可能对其他用途进行修改或应用。
很抱歉我的介绍太长了。现在，在编写实际程序之前，我想创建一些函数(使用def command)，以使实际代码更容易编写。到目前为止，我已经想到了这个:
import random
#Variables (Stop at (1) period of time or (2) above standard)
#number_rats = #initial population (standard)
min_offspring = 2
max_offspring = 10
#weight_rat = #Avg. of parents' weight (consider as single gender)
#mutation = #Randomly reduce the weight of small number of offspring (how many and how
much)
def breeding(female, male):
#random # of offspring and random weight for each
offspring = []
number_offspring = random.randint(min_offspring, max_offspring)
for i in range(0, number_offspring, 1):
offspring.append(random.triangular(female, male))
return offspring
def #random pair
def # cut offspring population to number_rats (list from highest weight to lowest and
cut light rats)
def # negative mutation (maybe inside breeding function) 10% chance of mutation that
decreases weight by 10%.
我尝试用2只老鼠(我标记了雄性和雌性，但我决定任何2只老鼠都可以减少复杂性)来完成繁殖功能，它们会产生随机数量的后代。
然而，我坚持为(1)从number_rats中获取随机对，(2)将大量后代切割为== number_rat，(3)在繁殖时产生负突变创建函数。如果能把这些函数写得简洁明了，对我的帮助就很大了。
我知道这篇文章很长，工作不完整，但帮助我完成这个初始阶段将极大地帮助我。如果这不是高级工作，我很抱歉。
无论如何，提前感谢。
试试这个，你可以对老鼠的初始数量和世代等进行实验。有一个类Rat，它定义了一个具有名称和权重的Rat，用于跟踪权重。
代码import random
import string
num_rats = 20
min_offspring = 2
max_offspring = 10
init_weight_min = 1
init_weight_max = 10
mutation_chance_rate = 10 # %
mutation_weight_decrease = 10 # %
generations = 4
alphabet = string.ascii_lowercase + string.digits
def random_name():
"""Generates random chars for rat names."""
return ''.join(random.choices(alphabet, k=8))
def print_population(population):
for i, r in enumerate(population):
print(f'no. {i+1:02d}, name: {r.name}, weight: {r.weight}')
class Rat:
def __init__(self, name, weight):
self.name = name
self.weight = weight
def breeding(p1, p2):
"""
Create offsprings from parents p1 and p2.
Randomize number of offsprings and weight is based on parents weight.
"""
offspring = []
w1 = p1.weight
w2 = p2.weight
meanw = (w1+w2)/2
number_offspring = random.randint(min_offspring, max_offspring)
for _ in range(1, number_offspring):
name = random_name()
weight = meanw
offspring.append(Rat(name, weight)) # Create Rat object as new pop
return offspring
def random_pair():
pass
def cut_offspring_population(population):
"""
population is a list of rats.
Cut offsprings to orignal number of rats, preserve heavier rats.
"""
new_population = sorted(population, key=lambda x: x.weight, reverse=True) # sort rats weights descending
return new_population[0:num_rats] # Cutoff
def negative_mutation(population):
"""
10% chance of mutation that decreases weight by 10%.
"""
new_population = []
for p in population:
current_name = p.name
current_weight = p.weight
if random.randint(1, 100) <= mutation_chance_rate:
current_weight = current_weight * (100 - mutation_weight_decrease) / 100
new_population.append(Rat(current_name, current_weight))
else:
new_population.append(Rat(current_name, current_weight))
return new_population
def main():
# (1) Create rats
orig_rats = []
for _ in range(num_rats):
orig_rats.append(Rat(random_name(), random.randint(init_weight_min, init_weight_max)))
random.shuffle(orig_rats)
tmp_rats = orig_rats.copy()
for i in range(1, generations + 1):
# (2) Breeding
new_offs = []
while tmp_rats:
# Select 2 rats as parents.
a = tmp_rats.pop()
b = tmp_rats.pop()
offs = breeding(a, b)
new_offs = new_offs + offs # save all new offsprings in a list.
# (3) Reduce population.
reduced_pop = cut_offspring_population(new_offs)
# (4) Mutation
mutated_pop = negative_mutation(reduced_pop)
print(f'gen: {i}')
print_population(mutated_pop)
print()
tmp_rats = mutated_pop.copy() # for next gen
if __name__ == "__main__":
main()
4代后的输出
后代体重为父母体重的平均值。老鼠的名字只是随机的。
gen: 1
no. 01, name: gzqru5c7, weight: 10.0
no. 02, name: ngpqx75q, weight: 10.0
no. 03, name: 3f2f8ua9, weight: 10.0
no. 04, name: uitaftbs, weight: 9.0
no. 05, name: dkr2dmyg, weight: 9.0
no. 06, name: lq350zck, weight: 8.0
no. 07, name: 4l08ks0t, weight: 8.0
no. 08, name: 1sl64mzl, weight: 7.5
no. 09, name: 88umsinn, weight: 7.5
no. 10, name: 3f30jp8m, weight: 7.5
no. 11, name: y1gbmbyn, weight: 7.5
no. 12, name: j7w7fr9y, weight: 7.5
no. 13, name: 3x5gl7zt, weight: 7.5
no. 14, name: 7mus480j, weight: 7.5
no. 15, name: 8yaifbuf, weight: 7.5
no. 16, name: t14n1qyq, weight: 7.5
no. 17, name: pqtieh8h, weight: 7.0
no. 18, name: 2eb1rhax, weight: 7.0
no. 19, name: ekfhcwye, weight: 7.0
no. 20, name: gdmeu1td, weight: 7.0
gen: 2
no. 01, name: nod75vx3, weight: 10.0
no. 02, name: pbe2z04b, weight: 10.0
no. 03, name: 1gn30dch, weight: 9.0
no. 04, name: txj11vza, weight: 10.0
no. 05, name: eonla5xu, weight: 9.0
no. 06, name: kwh5uffh, weight: 10.0
no. 07, name: pcvw8djm, weight: 10.0
no. 08, name: 7upmw4bu, weight: 9.0
no. 09, name: 3yb36bfr, weight: 10.0
no. 10, name: sjp0m8n8, weight: 10.0
no. 11, name: yj5oyuwd, weight: 9.5
no. 12, name: hsnbhyy7, weight: 9.5
no. 13, name: 40bpj2jw, weight: 9.5
no. 14, name: 4cdsgb4l, weight: 9.5
no. 15, name: 4lutoxh7, weight: 9.5
no. 16, name: s1111jrc, weight: 9.5
no. 17, name: le2m1x6w, weight: 7.65
no. 18, name: m2t9tfas, weight: 8.5
no. 19, name: r1gzn6a7, weight: 8.5
no. 20, name: lmvntp28, weight: 8.5
gen: 3
no. 01, name: 6g42b95g, weight: 10.0
no. 02, name: end7366f, weight: 10.0
no. 03, name: ccivuw0g, weight: 9.0
no. 04, name: rpf9pd51, weight: 10.0
no. 05, name: 94qkveea, weight: 10.0
no. 06, name: x1p9rd00, weight: 10.0
no. 07, name: v4d39x6t, weight: 10.0
no. 08, name: z3miwqoy, weight: 10.0
no. 09, name: vmkkrkqt, weight: 10.0
no. 10, name: ii8is1xp, weight: 10.0
no. 11, name: uadfjnng, weight: 10.0
no. 12, name: 5349eie7, weight: 10.0
no. 13, name: ikpoyce6, weight: 10.0
no. 14, name: yqqgsm9p, weight: 10.0
no. 15, name: ykkq03jv, weight: 10.0
no. 16, name: i3zzdab2, weight: 10.0
no. 17, name: 1m7kjzom, weight: 9.0
no. 18, name: vqatmrar, weight: 10.0
no. 19, name: 6ddudyf7, weight: 9.5
no. 20, name: 5b9dhzwp, weight: 9.5
gen: 4
no. 01, name: mm0iggdw, weight: 10.0
no. 02, name: u6evuhn8, weight: 9.0
no. 03, name: e0jo12tu, weight: 9.0
no. 04, name: wbage11q, weight: 10.0
no. 05, name: 4zlf1gvx, weight: 10.0
no. 06, name: 1c2hr5dd, weight: 10.0
no. 07, name: hbyzhpfn, weight: 10.0
no. 08, name: avf5ptk5, weight: 10.0
no. 09, name: hgurh5l0, weight: 10.0
no. 10, name: crqyuao0, weight: 10.0
no. 11, name: vjxkf3qf, weight: 10.0
no. 12, name: myzdj95e, weight: 9.0
no. 13, name: 8v4g3wxz, weight: 10.0
no. 14, name: l0z17ijw, weight: 10.0
no. 15, name: 1z3brmra, weight: 10.0
no. 16, name: r261q7pr, weight: 10.0
no. 17, name: ovl7vla5, weight: 10.0
no. 18, name: f2mvcvyw, weight: 10.0
no. 19, name: u1x8b7il, weight: 9.0
no. 20, name: l5k43dut, weight: 10.0
每代平均重量打印代码
基于代码的一代完成突变后。所以我把代码放在变异后面。
插入以下代码，从# Print average.
开始tmp_rats = mutated_pop.copy() # for next gen
# Print average.
rwt = []
for p in tmp_rats: # [Rat('aaa', 10), Rat('bbb', 8) ...]
rwt.append(p.weight)
aw = sum(rwt) / len(rwt)
print(f'average weight: {aw:0.2f}') # :0.2f is up to 2 decimal places
print()