Improving and Scaling Evolutionary Approaches to the MasterMind Problem
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Presentation for Evostar 2011 for the paper by Juan J. Merelo , Carlos Cotta , and Antonio Mora
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Improving and Scaling Evolutionary Approaches to the MasterMind Problem
- 1. J. J. Merelo , Carlos Cotta, Antonio Mora U. Granada & Málaga (Spain) Improving and Scaling Evolutionary Approaches to the MasterMind Problem
- 4. NP-Complete
- 8. Optimal solution not known
- 10. Let's play, then
- 15. Some will reduce the remaining search space more.
- 16. But scoring them is an open issue.
- 18. What we do now Introduce endgames and evaluate several problem sizes
- 19. How do we use endgames? By changing our strategy after certain answers from codemaker
- 20. Endgame: All Colors Use only permutations of combination
- 21. Endgame: Null combination Exclude those colors from all combinations and reduce population accordingly.
- 22. Results
- 23. Endgames improve algorithmic performance and game-playing quality
- 24. Open source your science!
Notes de l'éditeur
- I will always remember one of the first reviews I received on a paper on Mastermind, 15 or so years ago: Why would anybody care about mastermind?
- How would you play mastermind? It's not easy to do, since possible branches are many more than for Sudoku or even chess. In fact, this is the kind of game that can be played more easily by a machine than by a person. CC picture from http://www.flickr.com/photos/unloveable/2399932549/
- One of the possible ways to find solutions. Could be others, of course, but this is a good one.
- Let's just say that what we do is, once a solution is consistent, we find a scoring based on how the set of consistent solutions is partitioned by comparing consistent solutions with each other. In other papers we tested different ways of doing it, and we're fixing it here. Ideally, anyways, the solution should have always the maximum fitness, but I'm not sure it does (it will have to be checked)
- Creative commons image from Okinawa Soba at http://www.flickr.com/photos/24443965@N08/3606831198/ This was published in NICSO, Evostar, CIG, GECCO (as a póster) and eventually PPSN
- CC Picture from San Diego Shooter http://www.flickr.com/photos/nathaninsandiego/3758988303/
- Excelent picture from http://www.flickr.com/photos/jeremymates/3090959981/lightbox/ with a CC licence This change of strategy is a heuristic method, which would make this possibly a memetic algorithm.
- One of the possible ways to find solutions. Could be others, of course, but this is a good one.
- One of the possible ways to find solutions. Could be others, of course, but this is a good one.
- EvoRank-EG is the new algorithm with endgames; the rest have been included to see how it scaled to bigger sizes (and how difference scaled, too)
- Picture from http://www.flickr.com/photos/20262161@N00/3301968244/in/photostream/ with a CC licence That means that, in general, heuristic methods help constrain search and are a boon for evolutionary algorithms
- All source, data sets, experiment results for this paper are available from Sourceforge (in fact, they were while we were doing it). Source is also available from the CPAN Perl module server worldwide, in two separate modules: the algorithm itself as the module Algorithm::Mastermind (along with other algorithms; for instance, Knuth's algorithm), and the EA in the shape of the Evolutionary Algorithm library.