Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

- An Algorithm for solving the game o... by Juan J. Merelo 24024 views
- Adapting Heuristic Mastermind Strat... by Juan J. Merelo 2554 views
- Poster: A search for scalable evolu... by Juan J. Merelo 1514 views
- Improving and Scaling Evolutionary ... by Juan J. Merelo 1506 views
- Póster: Comparing evolutionary algo... by Juan J. Merelo 2233 views

2,052 views

Published on

Published in:
Technology

No Downloads

Total views

2,052

On SlideShare

0

From Embeds

0

Number of Embeds

545

Shares

0

Downloads

9

Comments

0

Likes

1

No embeds

No notes for slide

- 1. J. J. Merelo , Carlos Cotta, Antonio Mora U. Granada & Málaga (Spain) Http://geneura.wordpress.com http://twitter.com/geneura Optimizing worst-case scenario in evolutionary solutions to the MasterMind puzzle
- 2. Game of MasterMind
- 3. 7 reasons why you should care <ul><li>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 </li></ul>
- 10. Let's play, then
- 11. Consistent combinations
- 12. Naïve Algorithm <ul><li>Repeat </li><ul><li>Find a consistent combination and play it. </li></ul></ul>
- 13. Looking for consistent solutions <ul><li>Optimization algorithm based on distance to consistency (for all combinations played) </li></ul>D = 2
- 14. Not all consistent combinations are born the same <ul><li>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. </li></ul>
- 17. What we did before <ul>Apply heuristic methods to speed up finishing games </ul>
- 18. What we do now Increase diversity in search via new operators and selection mechanisms
- 19. Objectives <ul><li>Reduce the probability of takeover by a single individual
- 20. Reduce the possibility of repeated generation of a single combination
- 21. Increase speed to afford tackling bigger sizes </li></ul>
- 22. New tricks for old games <ul><li>Add permutation operator
- 23. Add diff uniform crossover </li><ul><li>crossover over different positions only
- 24. Selection makes sure parents are different </li></ul><li>Change to tournament selection </li><ul><li>Stronger selective pressure </li></ul><li>Higher replacement rate </li></ul>
- 25. Results: number of evaluations
- 26. Results: any good at MasterMind? Better Better
- 27. Mission accomplished?
- 28. Worst case is better! Measures to increase diversity have a positive impact on quality of algorithm and also speed
- 29. Open source your science!
- 30. Thank you very much Questions?

No public clipboards found for this slide

Be the first to comment