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 numberimport random # function for implementing the single-point crossoverdef 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 crossoverfor 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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