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

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Game of MasterMind

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7 reasons why you should care

Donald Knuth

NP-Complete

Differential cryptanalisis/ATM cracking

Circuit/program test

Genetic profiling

Optimal solution not known

Interesting search problem

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Let's play, then

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Consistent combinations

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Naïve Algorithm

Repeat

Find a consistent combination and play it.

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Looking for consistent solutions

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

D = 2

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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.

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What we did beforeWhat we did before

Apply heuristic methods to speed up finishing Apply heuristic methods to speed up finishing gamesgames

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What we do now

Increase diversity in search via new operators and selection mechanisms

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Objectives

Reduce the probability of takeover by a single individual

Reduce the possibility of repeated generation of a single combination

Increase speed to afford tackling bigger sizes

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New tricks for old games

Add permutation operator

Add diff uniform crossover

crossover over different positions only

Selection makes sure parents are different

Change to tournament selection

Stronger selective pressure

Higher replacement rate

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Results: number of evaluations

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Results: any good at MasterMind?

Better

Bet

ter

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Mission accomplished?

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Worst case is better!

Measures to increase diversity have a positive impact on quality of algorithm and also speed

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Open source your science!

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Thank you very much

Questions?