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Python | Single Point Crossover in Genetic Algorithm

Last Updated : 13 Oct, 2019

Single Point Crossover in Genetic Algorithm is a form of crossover in which two-parent chromosome are selected and a random/given point is selected and the genes/data are interchanged between them after the given/selected point for example

Examples:

P1: 000011110011 
P2: 101010101010

Point: 4
After Crossover:
C1: 000010101010
C2: 101011110011

The problem is to select a random point for the crossover of two given parents and generate at least five generations of children from the given pair of a chromosome.

Code : Python program for single-point crossover in Genetic Algorithm

# library to generate a random number 
import random 
  
# function for implementing the single-point crossover 
def crossover(l, q): 
  
# converting the string to list for performing the crossover 
    l = list(l) 
    q = list(q) 
  
# generating the random number to perform crossover 
    k = random.randint(0, 15) 
    print("Crossover point :", k) 
  
# interchanging the genes 
    for i in range(k, len(s)): 
        l[i], q[i] = q[i], l[i] 
    l = ''.join(l) 
    q = ''.join(q) 
    print(l) 
    print(q, "\n\n") 
    return l, q 
  
  
# patent chromosomes: 
  
s = '1100110110110011'
p = '1000110011011111'
print("Parents") 
print("P1 :", s) 
print("P2 :", p, "\n") 
  
# function calling and storing the off springs for  
# next generation crossover 
for i in range(5): 
    print("Generation ", i+1, "Childrens :") 
    s, p = crossover(s, p) 

Output:

Parents
P1 : 1100110110110011
P2 : 1000110011011111 

Generation  1 Childrens :
Crossover point : 2
1100110011011111
1000110110110011 


Generation  2 Childrens :
Crossover point : 7
1100110110110011
1000110011011111 


Generation  3 Childrens :
Crossover point : 0
1000110011011111
1100110110110011 


Generation  4 Childrens :
Crossover point : 7
1000110110110011
1100110011011111 


Generation  5 Childrens :
Crossover point : 2
1000110011011111
1100110110110011

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