A restarted estimation of distribution algorithm for solving sudoku puzzles
-
Sylvain Maire
und
Cyril Prissette
Abstract.
In this paper, we describe a stochastic algorithm to solve sudoku puzzles. Our method consists in computing probabilities for each symbol of each cell updated at each step of the algorithm using estimation of distributions algorithms (EDA). This update is done using the empirical estimators of these probabilities for a fraction of the best puzzles according to a cost function. We develop also some partial restart techniques in the RESEDA algorithm to obtain a convergence for the most difficult puzzles. Our algorithm is tested numerically on puzzles with various levels of difficulty starting from very easy ones to very hard ones including the famous puzzle AI Escargot. The CPU times vary from few hundreds of a second for the easy ones to about one minute for the most difficult one.
© 2012 by Walter de Gruyter Berlin Boston
Artikel in diesem Heft
- Masthead
- On the population median estimation using robust extreme ranked set sampling
- A mathematical formalization of the parallel replica dynamics
- A restarted estimation of distribution algorithm for solving sudoku puzzles
- Parallel random number generators in Monte Carlo derivative pricing: An application-based test
- Probabilistic error bounds for the discrepancy of mixed sequences
Artikel in diesem Heft
- Masthead
- On the population median estimation using robust extreme ranked set sampling
- A mathematical formalization of the parallel replica dynamics
- A restarted estimation of distribution algorithm for solving sudoku puzzles
- Parallel random number generators in Monte Carlo derivative pricing: An application-based test
- Probabilistic error bounds for the discrepancy of mixed sequences
