Mastermind by evolutionary algorithms
Proceedings of the 1999 ACM symposium on Applied computing
Competitive Environments Evolve Better Solutions for Complex Tasks
Proceedings of the 5th International Conference on Genetic Algorithms
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
Improving and scaling evolutionary approaches to the mastermind problem
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
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
| Hi-index | 0.00 |
Mastermind is a well-known board game in which one player must discover a hidden color combination set up by an opponent, using the hints 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). This game has attracted much theoretical attention, since it constitutes a very interesting example of dynamically-constrained combinatorial problem, in which the set of feasible solutions changes with each combination played. We present an evolutionary approach to this problem whose main features are the seeded initialization of the population using feasible solutions discovered in the previous move, and the use of an entropy-based criterion to discern among feasible solutions. This criterion is aimed at maximizing the information that will be returned by the opponent upon playing a combination. Three variants of this approach, respectively based on the use of a single population and two cooperating or competing subpopulations are considered. It is shown that these variants achieve the playing level of previous state-of-the-art evolutionary approaches using much lower computational effort (as measured by the number of evaluations required).