# Different approaches of genetic algorithm

I wrote a code that implements a simple genetic algorithm to maximize: $$f(x) = 15x - x^2.$$ The function has its maximum at $x=7.5$, so the code output should be $7$ or $8$ since the population are integers. When I run the code ten times I get $7$ or $8$ around three times out of $10$. I chose mating probability of $0.7$ and mutation probability of $0.001$. What modification should I make to further improve the algorithm and what are different types of genetic algorithms?

Here is the code:

from random import *
import numpy as np

#fitness function
def fit(x):
return 15*x -x**2
#covert binary list to decimal number
def to_dec(x):
return int("".join(str(e) for e in x), 2)
#picks pairs from the original population
def gen_pairs(populationl, prob):
pairsl = []
test = [0, 1, 2, 3, 4, 5]
for i in range(3):
pair = []
for j in range(2):
temp = np.random.choice(test, p=prob)
pair.append(populationl[temp].copy())
pairsl.append(pair)

return pairsl
#mating function
def cross_over(prs, mp):
new = []
for pr in prs:
if mp[prs.index(pr)] == 1:
index = np.random.choice([1,2,3], p=[1/3, 1/3, 1/3])
pr[:index], pr[:index] = pr[:index], pr[:index]

for pr in prs:
new.append(pr)
new.append(pr)

return new

#mutation
def mutation(x):
for chromosome in x:
for gene in chromosome:
mutation_prob = np.random.choice([0, 1], p=[0.999, .001])
if mutation_prob == 1:
#m_index = np.random.choice([0,1,2,3])
if gene == 0:
gene = 1
else:
gene = 0

#generate initial population
randlist = lambda n:[randint(0,1) for b in range(1, n+1)]
for j in range(10):
population = [randlist(4) for i in range(6)]
for _ in range(20):
fittness = [fit(to_dec(y)) for y in population]

s = sum(fittness)
prob = [e/s for e in fittness]
pairsg = gen_pairs(population.copy(), prob)

mating_prob = []
for i in pairsg:
mating_prob.append(np.random.choice([0,1], p=[0.4,0.6]))

new_population = cross_over(pairsg, mating_prob)
mutated = mutation(new_population)
decimal_p = [to_dec(i)for i in population]
decimal_new = [to_dec(i)for i in new_population]
# print(decimal_p)
# print(decimal_new)
population = new_population
print(decimal_new)

• This seems to be better for Stack Overflow or some other sites… – Saad Jun 1 '18 at 17:22
• @Alex Francisco I asked on Satckoverflow, someone said I should post it here :P – Mohammed Obeidat Jun 1 '18 at 18:36
• Ask it on Math Overflow? – ray lin Jun 1 '18 at 21:56
• Math overflow is not at all the place for this. Anyway genetic algorithms are a lot of fun. Unfortunately this code is hard to read because there aren't any comments and the variable names aren't great. – Cameron Williams Jun 2 '18 at 1:41