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I've taken on the task of creating a sudoku solver using a genetic algorithm. Initialization : Store the given values in each chromosome, and then randomly generate values such that each row is a valid permutation of the values 1 through 9. Fitness : Determined by the number of "out of place" values in each row, column, and square grid, added together. Fitness Function : Typical roulette wheel selection Selection : Random, but weighted using the roulette wheel. Crossover : Randomly choose various rows from two parents, which creates one child. (I've also implemented a crossover that randomly chooses 3 rows at a time from the two parents - in an effort to preserve good mini-grids). The following are two example children, one from each crossover method: Parent 1 row 1 Parent 2 row 2 Parent 1 row 3 Parent 2 row 4 Parent 1 row 5 Parent 2 row 6 Parent 2 row 7 Parent 1 row 8 Parent 1 row 9 Parent 1 row 1 Parent 1 row 2 Parent 1 row 3 Parent 2 row 4 Parent 2 row 5 Parent 2 row 6 Parent 1 row 7 Parent 1 row 8 Parent 1 row 9 Mutation : Initially I just swapped the values at two random locations, but this actually made the algorithm much worse because it introduced duplications in rows which had been valid permutations. So I altered the mutation (which seems to perform best when the chance of mutation is in the 25% - 50% range) to randomly choose a row, and then randomize the ordering of that row (leaving the given values in their correct locations). I also tried a mutation where it chose a random row and then chose two random (non-given) positions in the row and swapped them, but this made the performance much worse as well. (Unlike the swapping of the two random locations, I don't understand why this mutation would make the performance so much worse, yet a mutation to randomize the entire row ma

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