This document summarizes research into using evolutionary algorithms to solve the Mastermind puzzle more efficiently than exhaustive search or naive algorithms. The researchers tested canonical genetic algorithms and estimation of distribution algorithms on the problem, finding that these evolutionary approaches obtained results comparable or better than exhaustive search with less computational effort. Their approach involved using distance-to-consistency as the fitness function and scoring and selecting consistent combinations to guide the search. The evolutionary algorithms were able to defeat exhaustive search with similar running times to naive approaches.

1. J. J. Merelo , T. P. Runarsson U. Granada (Spain) & U. Iceland Finding better solutions to the Mastermind puzzle using evolutionary algorithms

2. Game of MasterMind

3. Why should I care? I want to frag!

4. 7 reasons why you should Donald Knuth

5. NP-Complete

6. Differential cryptanalisis

7. Circuit and program test

8. Genetic profiling

9. Minimize guesses

10. Minimize evaluations

11. Let's play, then

12. Consistent combinations

13. Naïve Algorithm Repeat Find a consistent combination and play it.

14. Looking for consistent solutions Optimization algorithm based on distance to consistency (for all combinations played) D = 2

15. Not all consistent combinations are born the same There's at least one better than the others (the solution)

16. Some will reduce the remaining search space more.

17. But scoring them is an open issue

18. Score consistent set

19. Enter partitions Most parts. Score = 5 Best worst case. Score = -3 Entropy. Score = 1.67 1.31 0.96 0.96 1.58 1.52 1.67 1.42 1.52 1.67

20. Less Naïve Algorithm Play initial combination