Mastermind by evolutionary algorithms
Proceedings of the 1999 ACM symposium on Applied computing
Solving Master Mind Using GAs and Simulated Annealing: A Case of Dynamic Constraint Optimization
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A Two-Phase Optimization Algorithm For Mastermind
The Computer Journal
Efficient solutions for Mastermind using genetic algorithms
Computers and Operations Research
On the algorithmic complexity of the Mastermind game with black-peg results
Information Processing Letters
Solving mastermind using genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Cracking bank PINs by playing mastermind
FUN'10 Proceedings of the 5th international conference on Fun with algorithms
Entropy-driven evolutionary approaches to the mastermind problem
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Improving evolutionary solutions to the game of mastermind using an entropy-based scoring method
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Mastermind is a well-known board game in which one player must discover a hidden combination of colored pegs set up by an opponent, using the hints that the latter provides (the number of places -or pegs- correctly guessed, and the number of colors rightly guessed but out of place) in each move. The feasibility of evolutionary approaches to solve this problem has been already proved; in this paper we will assess different methods to improve the time it takes to find a solution by introducing endgames, that is, shortcuts for finding the solution when certain circumstances arise. Besides, we will measure the scalability of the evolutionary approaches by solving generalized Mastermind instances in several sizes. Tests show that endgames improve the average number of solutions without any influence on the quality of the game; at the same time, it speeds up solutions so that bigger problems can be approached. Tests performed with eight colors and four or five pegs and nine colors with five pegs show that scaling is quite good, and that the methodology yields an average number of games that is competitive with the best solutions published so far. Scaling with problem size depends on the method, being better for entropy-based solutions, but -besides raw problem size-there are complex dependencies on the number of pegs and colors.