1. 1. J. J. Merelo , Carlos Cotta, Antonio Mora U. Granada & Mlaga (Spain) Improving and Scaling Evolutionary Approaches to the MasterMind Problem
2. 2. Game of MasterMind
3. 3. 7 reasons why you should care
   • Donald Knuth
   • 4. NP-Complete
   • 5. Differential cryptanalisis/ATM cracking
   • 6. Circuit/program test
   • 7. Genetic profiling
   • 8. Optimal solution not known
   • 9. Interesting search problem
4. 10. Let's play, then
5. 11. Consistent combinations
6. 12. Nave Algorithm
   • Repeat
     • Find a consistent combination and play it.
7. 13. Looking for consistent solutions
   • Optimization algorithm based on distance to consistency (for all combinations played)
   D = 2
8. 14. Not all consistent combinations are born the same
   • There's at least one better than the others (the solution).
   • 15. Some will reduce the remaining search space more.
   • 16. But scoring them is an open issue.
9. 17. What we did before
   • Play using evolutionary and co-evolutionary algorithms, fitness uses a sub-set of consistent combination
10. 18. What we do now Introduceendgamesand evaluate several problem sizes
11. 19. How do we use endgames? By changing our strategy after certain answers from codemaker
12. 20. Endgame: All Colors Use onlypermutationsof combination
13. 21. Endgame:Nullcombination Excludethose colorsfrom all combinations and reduce population accordingly.
14. 22. Results
15. 23. Endgamesimprove algorithmicperformance and game-playingquality
16. 24. Open source your science!