Solving Sudoku with genetic algorithms

While genetic algorithms are useful for solving certain problems, Sudoku puzzles exhibit a strong combinatorial structure that can be exploited by other approaches (Constraint Programming above all, I think).<p>When modeled as feasibility problems, for instance, their solution can be found very quickly -- it took 0.2 seconds to solve that instance on my laptop.

"I think a Sudoku puzzle that is harder for humans would not be that much harder for optopus to solve, but I have not tested it."<p>Try it. You may be surprised. Here's a good one: zonkedyak.blogspot.com/2006/11/worlds-hardest-sudoku-puzzle-al.html<p>Also try a 5-based instance of the same problem (against the standard 3-base of sudoku, that is, a 5x5 matrix of 5x5 matrices containing the numbers 1-25), and compare performance of that problem to a normal solver.<p>I'm not going to tell you what will happen, because you won't believe me. Somebody seems to have cast a glamor on GP.

Plus one for posts about Genetic Algorithms. My masters research was on Genetic Algorithms. Btw for those interested in learning more about the field my professor has a free book out on Metaheuristics (Which includes genetic algorithms) at <a href="http://cs.gmu.edu/~sean/book/metaheuristics/" rel="nofollow">http://cs.gmu.edu/~sean/book/metaheuristics/</a>

Isn't sudoku bit problematic problem for GA as there is just one correct solution and the rest are just wrong, compared to a problem that has solutions that are better or worse, but potentially no solution is absolutely correct?