J. J. Merelo, A. Mora, C. Cotta, T. Runrsson U. Granada & Mlaga (Spain) & IcelandHttp://geneura.wordpress.comhttp://twitter.com/geneura

Optimizing search via diversity enhancement in evolutionary MasterMind

Game of MasterMind

Let's play, then

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/

Consistent combinations

One of the possible ways to find solutions. Could be others, of course, but this is a good one.

Nave Algorithm

RepeatFind a consistent combination and play it.

Looking for consistent solutions

Optimization algorithm based on distance to consistency (for all combinations played)

D = 2

Not all consistent combinations are born the same

There's at least one better than the others (the solution).

Some will reduce the remaining search space more.

But scoring them is an open issue.

Like the birds. They look the same, but one of them has a bad hair day. Or rather a bad feather day.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)

What we did before

Increase diversity in search via new operators and selection mechanisms

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 pster) and eventually PPSN

What we do now

Fine-tune evolutionary parameters to minimize evaluations and number of games played

CC Picture from San Diego Shooter http://www.flickr.com/photos/nathaninsandiego/3758988303/New is always better. And better is also always better. Mostly.

Increase diversity.

Increase speed to afford tackling bigger sizes.

Obtain better solutionsLess turns

Picture from Philip James Claxton at http://www.flickr.com/photos/philipclaxton/4076919342/in/photostream/

Consistent set size

Tournament size

Fine tuned!

#Evaluations decreased up to 30%!(Game performance still the same)

Image from John Traynor at http://www.flickr.com/photos/trainor/3028243647/in/photostream/

Open source your science!

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.

Thank you very much

Questions?

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