Adaptive search engines as discovery games: an evolutionary approach
Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia
The number of pessimistic guesses in Generalized Mastermind
Information Processing Letters
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 and scaling evolutionary approaches to the mastermind problem
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Optimal analyses for 3×n AB games in the worst case
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
The number of pessimistic guesses in Generalized Black-peg Mastermind
Information Processing Letters
Comparing evolutionary algorithms to solve the game of mastermind
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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This paper presents a systematic model, two-phase optimization algorithms (TPOA), for Mastermind. TPOA is not only able to efficiently obtain approximate results but also effectively discover results that are getting closer to the optima. This systematic approach could be regarded as a general improver for heuristics. That is, given a constructive heuristic, TPOA has a higher chance to obtain results better than those obtained by the heuristic. Moreover, it sometimes can achieve optimal results that are difficult to find by the given heuristic. Experimental results show that (i) TPOA with parameter setting (k, d) = (1, 1) is able to obtain the optimal result for the game in the worst case, where k is the branching factor and d is the exploration depth of the search space. (ii) Using a simple heuristic, TPOA achieves the optimal result for the game in the expected case with (k, d) = (180, 2). This is the first approximate approach to achieve the optimal result in the expected case.