This document describes the steps of a genetic algorithm to solve the N-queen problem. It starts with an initial population of random board configurations. It then 1) calculates the fitness of each configuration by counting the number of queens under attack, 2) selects configurations for reproduction based on fitness, 3) performs a crossover operation by swapping elements between pairs of selected configurations, 4) performs a random mutation by changing a random element of a configuration, and 5) repeats these steps until finding a configuration with a fitness of 0, indicating no attacking queens.

1. N-queen
genetic algorithm

2. Empty board

3. G0 1select
(0(0,3,1,3

4. (1,1,2,0)1

5. (2,3,1,1)2

6. (1,2,3,0)3

7. 2fitness
 By counting number of attack queen for each
Queen.
Basic fitness=n*(n-1)=12/2=6
0(0,3,1,3)1+1+0+2=4/2=2 fitness=6-2=4
1(1,1,2,0)1+2+1+0=4/2=2 fitness=6-2=4
2(2,3,1,1)1+1+1+2=5/2=2.5 fitness=6-2.5=3.5
and so on

8. 3Selection
Select 2 generation random or high fitness
(0,3,1,3) (2,3,1,1 )
Then choose same random index for two
solution index=1
Replacement array1 from index+1 to n-1 with
array2
(1,3)< == >(1,1)
(0,3,1,1) (2,3,1,3)

9. (0,3,1,1) (2,3,1,3)

10. 4cross over
2 generation random or high fitness
(0,3,1,3) (2,3,1,1 )
Then choose same random index for two
solution index=2
Replacement this element with
corresponding element on the same index
1< == > 1
(0,3,1,3) (2,3,1,1 )

11. 5mutation
• Select random solution
For example: (0,3,1,3) select a random index
and replace this element with a random value
from(0,1,2,3)
Index =1 replacement with 2
.: solution (0,2,1,3)

12. When we finished this steps
if fitness==0
• Then I’ have a solution