Python 遗传算法：设置函数以供使用
Python Genetic Algorithm: Setting Functions for Usage
我正在试验 python 遗传算法。换句话说，我想制作一个程序来模拟随机过程和控制部分（如实验）来处理变量和结果。
对于我的程序，我正在模拟老鼠繁殖并控制其数量以在最后创造出更好的（在我的例子中，更重的）老鼠。具体来说，我想要老鼠的起始种群，随机配对两只进行繁殖（其中后代将具有两只母鼠的平均体重）并控制后代种群等。简而言之，我将拥有 x 只老鼠（初始和常量），创造后代，按重量列出它们，从底部杀死（最轻的老鼠）以降低后代数量 == 初始数量并重复该过程以最终创造更重的老鼠老鼠.
我知道这个实验并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，并可能对其他用法进行修改或应用。
抱歉介绍太长了。现在，在编写实际程序之前，我想创建函数（使用 def 命令）以使实际代码更易于编写。到目前为止，我想出了这个：
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%.
我已经尝试完成繁殖功能以取 2 只老鼠（我标记为雄性和雌性，但我决定任何 2 只老鼠都可以减少并发症）并且它们会产生随机数量的后代。
但是，我一直在为以下内容创建函数：(1) 从 number_rats 中随机取对，(2) 将大量后代切割成 == number_rat，以及 (3) 创建负数繁殖时突变。帮我把这些函数写的简洁一些，对我有很大的帮助。
我知道这很长 post，工作还未完成，但在这个初始阶段帮助我将对我有很大帮助。如果这不是高级工作，我很抱歉。
无论如何，先谢谢了。
试试这个，您可以对大鼠的初始数量和世代等进行实验。有一个 class Rat 定义了一个带有名称和体重的老鼠，用于追踪体重。
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
后代体重是parents体重的平均值。老鼠的名字是随机的。
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
打印每代平均重量的代码
从代码来看，变异后完成了一代。所以我把代码放在突变之后。
从 # Print average 开始插入以下代码..
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()
我正在试验 python 遗传算法。换句话说，我想制作一个程序来模拟随机过程和控制部分（如实验）来处理变量和结果。
对于我的程序，我正在模拟老鼠繁殖并控制其数量以在最后创造出更好的（在我的例子中，更重的）老鼠。具体来说，我想要老鼠的起始种群，随机配对两只进行繁殖（其中后代将具有两只母鼠的平均体重）并控制后代种群等。简而言之，我将拥有 x 只老鼠（初始和常量），创造后代，按重量列出它们，从底部杀死（最轻的老鼠）以降低后代数量 == 初始数量并重复该过程以最终创造更重的老鼠老鼠.
我知道这个实验并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，并可能对其他用法进行修改或应用。
抱歉介绍太长了。现在，在编写实际程序之前，我想创建函数（使用 def 命令）以使实际代码更易于编写。到目前为止，我想出了这个：
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%.
我已经尝试完成繁殖功能以取 2 只老鼠（我标记为雄性和雌性，但我决定任何 2 只老鼠都可以减少并发症）并且它们会产生随机数量的后代。
但是，我一直在为以下内容创建函数：(1) 从 number_rats 中随机取对，(2) 将大量后代切割成 == number_rat，以及 (3) 创建负数繁殖时突变。帮我把这些函数写的简洁一些，对我有很大的帮助。
我知道这很长 post，工作还未完成，但在这个初始阶段帮助我将对我有很大帮助。如果这不是高级工作，我很抱歉。
无论如何，先谢谢了。
试试这个，您可以对大鼠的初始数量和世代等进行实验。有一个 class Rat 定义了一个带有名称和体重的老鼠，用于追踪体重。
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
后代体重是parents体重的平均值。老鼠的名字是随机的。
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
打印每代平均重量的代码
从代码来看，变异后完成了一代。所以我把代码放在突变之后。
从 # Print average 开始插入以下代码..
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()