Solving N-Queens problem with genetic algorithm.
- in parameters.txt there is an analysis for each parameter.
- I ran the code for 1000 times and made average and best case for each parameter.
run the code with python 2.
- We consider every case as gene like this : gene = [0,1,3,2,4,5,6,7]
- each line has one queen and gene[1] = 2 means we have a queen in row 1 and coloumn 2
- first we make some Genes (input as number_of_the_population) randomly and sort them by their fitness and select the best 50% of them.
- in each generation we make cross over and mutation
- each cross over and mutation has a probability.
- we make the number of cross overs half the size of the population
- this is how the mutation work: it takes two different random number between 0 and 8. it swap the to indexes in the gene so that a new gene creates and in a probability they have the chance to mutate or not.
- this is how the cross over works: it uses order recombination algorithm.
- this is how the fitness function works: find the number of crashes in a gene with checking different diagonals.
- we will add each new gene to our population.
- after the mutations and cross overs we take the best 50% genes sorted with their fitnesses.
- we stop the algorithm when the fitness is 0.