Also, I remember that I once read something from someone who tried a GA C-library on Sudoku and concluded that it was not a suitable problem. If I could solve it with my slick library, that random person on the internet, whose web page I might never find again but who may exist as far as you know, would certainly be proven wrong. A worthy cause.

Genetic algorithms

A genetic algorithm is a general way to solve optimization problems. The basic algorithm is very simple:

- Create a population (vector) of random solutions (represented in a problem specific way, but often a vector of floats or ints)
- Pick a few solutions and sort them according to fitness
- Replace the worst solution with a new solution, which is either a copy of the best solution, a mutation (perturbation) of the best solution, an entirely new randomized solution or a cross between the two best solutions. These are the most common evolutionary operators, but you could dream up others that use information from existing solutions to create new potentially good solutions.
- Check if you have a new global best fitness, if so, store the solution.
- If too many iterations go by without improvement, the entire population might be stuck in a local minimum (at the bottom of a local valley, with a possible chasm somewhere else, so to speak). If so, kill everyone and start over at 1.
- Go to 2.

There are other, subtly different, ways to perform the evolutionary process. Some are good and some are popular but bad. The one described above is called tournament selection and it is one of the good ways. Much can be said about the intricacies of GA but it will have to be said somewhere else, lest I digress completely.

Optopus and Sudoku

Using optopus is easy:

from optopus import ga, stdgenomes #Now we choose a representation. We know that the answer to the puzzle must be some permutation of the digits 1 to 9, each used nine times. init_vect = sum([range(1,10)] * 9, []) # A vector of 81 elements genome = stdgenomes.PermutateGenome (init_vect) #I made a few functions to calculate how many conflicts a potential Sudoku solution has. I'll show them later, but for now let us just import the package. I also found a puzzle somewhere and put it in the PUZZLE constant. import sudoku solver = ga.GA(sudoku.ga_sudoku(sudoku.PUZZLE) , genome) #And now, when we have supplied the GA with a fitness function (ga_sudoku, which counts Sudoku conflicts) and a representation (genome), let us just let the solver do its magic. solver.evolve(target_fitness=0)

And in a few minutes (about 2.6 million iterations when I tried) the correct answer pops out!

The nice thing about this method is that you do not have to know anything about how to solve a Sudoku puzzle or even think very hard at all. Note that I did not even bother to just let it search for the unknown values - it also has to find the digits that we already know (which should not be too hard with a decent fitness function, see below). The only bit of thinking we did was to understand that a Sudoku solution has to be a permutation of

*[1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9]*, but this merely made the evolution part faster. If we wanted to make it faster still, we could make a genome type that let us say that there are actually nine separate vectors who are each guaranteed to be a permutation of 1 to 9. We could have thought even less and represented the solution by 81 ints who are all in the range 1 to 9, by using another genome type:

>> genome = stdgenomes.EnumGenome(81, range(1,10))

The range argument to EnumGenome does not have to be a vector of integers, it could be a vector of any objects, since they are never treated like numbers.

In my experiment this took maybe 15 - 30 minutes to solve. For more difficult Sudoku puzzles, I would definitely go with the permutation genome, since using EnumGenome increases the search space to 9^81 or

*196627050475552913618075908526912116283103450944214766927315415537966391196809*possible solutions.

FYI, this is the puzzle in sudoku.PUZZLE:

**4|8 | 17**

**67 |9 |**

**5 8| 3 | 4**

**-----------**

**3 |74 |1**

**69| |78**

**1| 69| 5**

**-----------**

**1 | 8 |3 6**

**| 6| 91**

**24 | 1|5**

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.

Sudoku fitness function

OK, so that was a ridiculously easy way to solve a Sudoku puzzle, but I skipped one part that is crucial to all GA - describing the problem using a fitness function. I had to do the following:

DIM = 9 def one_box(solution, i): """Extract the 9 elements of a 3 x 3 box in a 9 x 9 Sudoku solution.""" return solution[i:i+3] + solution[i+9:i+12] + solution[i+18:i+21] def boxes(solution): """Divide a flat vector into vectors with 9 elements, representing 3 x 3 boxes in the corresponding 9 x 9 2D vector. These are the standard Sudoku boxes.""" return [one_box(solution, i) for i in [0, 3, 6, 27, 30, 33, 54, 57, 60]] def splitup(solution): """Take a flat vector and make it 2D""" return [solution[i * DIM:(i + 1) * DIM] for i in xrange(DIM)] def consistent(solution): """Check how many different elements there are in each row. Ideally there should be DIM different elements, if there are no duplicates.""" return sum(DIM - len(set(row)) for row in solution) def compare(xs1, xs2): """Compare two flat vectors and return how much they differ""" return sum(1 if x1 and x1 != x2 else 0 for x1, x2 in zip(xs1, xs2)) def sudoku_fitness(flatsolution, puzzle, flatpuzzle=None): """Evaluate the fitness of flatsolution.""" if not flatpuzzle: flatpuzzle = sum(puzzle, []) solution = splitup(flatsolution) fitness = consistent(solution) #check rows fitness += consistent(zip(*solution)) #check columns fitness += consistent(boxes(flatsolution)) #check boxes fitness += compare(flatpuzzle, flatsolution) * 10 #check that it matches the known digits return fitness def ga_sudoku(puzzle): """Return a fitness function wrapper that extracts the .genes attribute from an individual and sends it to sudoku_fitness.""" flatpuzzle = sum(puzzle, []) #just a precalcing optimization def fit(guy): return sudoku_fitness(guy.genes, puzzle, flatpuzzle) return fit

I know. This made the solution less clean. Still, I made it verbose for readability, so it is perhaps less code than it looks.

Take that, random internet guy!

Damn, This is mind numbing good...

ReplyDeleteThis is better:

ReplyDeletehttp://norvig.com/sudoku.html

@albert

ReplyDeleteI agree, that is a good approach.

Seems like genetic algorithms for sudoku solutions are a decent example of GA. But I think it might be fun/interesting to use GA to build some sort of logic engine to solve sudokus. :)

ReplyDeleteVery interesting way to solve sudoku.

ReplyDeleteI am just curious, exactly how long did it take for you to solve Sudoku?

I have a solution that solves Sudoku (among other tougher problems) very fast.

I just want to know your number before telling(bragging, as some might say) about mine.

I just wanna compare the speed of the two solutions, yours and mine.

Really good post.

Pretty cool, so did it solve one sudoku puzzle or did it evolve an algorithm to solve sudoku puzzles in general?

ReplyDeleteThanks for posting it!

So there is a GA to solve Sudoku. That means there must be a constructive algorithm to solve it too.

ReplyDeleteWhat is the constructive algorithm?

Human puzzle solvers and computers use very different approaches for Sudoku. Humans are good at finding patterns. Computers use brute force with genetic algorithms or other forms of recursive backtracking. Take a look at:

ReplyDeletehttp://www.mathworks.com/moler/exm/chapters/sudoku.pdf

I coded a Java GA for solving sudoku this year in a Meta-Heuristics course I took post grad. The best solving time I obtained from a few runs of AL Escargot was 9 minutes 41 seconds on a 2.66ghz core2 workstation, other problems took from sub one to 40 odd seconds depending on the difficulty. In the end the GA used four different methods of applying selection pressure, 4 more methods to then pare up parents, six different crossover methods, a modified island model and population restarting conditions... Needless to say comparitively to other meta-heuristics it is a very inefficient method.

ReplyDeleteIs there a built-in Genome that allows for a variable number of objects?

ReplyDeleteThanks! (:

@blob Unfortunately I have no variable length genomes in optopus, but it is easy enough to create one yourself. Look at BaseGenome and perhaps take some inspiration from FloatGenome.

ReplyDelete