Solving MasterMind using GAs and simulated annealing: A case of dynamic constraint optimization

Bernier, José Luis; Herráiz, C. Ilia; Guervós, Juan Julián Merelo; Olmeda, S.; Prieto, Alberto

doi:10.1007/3-540-61723-x_1019

Cited by 21 publications

(12 citation statements)

References 7 publications

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“…The variation operators were also adapted to these objects: a permutation and a creep operator, which substituted a number (color) by the next, and the last by the first. A huge improvement was obtained; the algorithm explored only 25% of the space that was explored before [5], that is, around 2% of the total search space, and thus obtained solutions much faster. The game can be played online at http://geneura.ugr.es/˜jmerelo/GenMM; the code can be downloaded from the same site.…”

Section: Applicationsmentioning

confidence: 95%

Evolving Objects: A General Purpose Evolutionary Computation Library

Keijzer¹,

Guervós

Romero

et al. 2002

Lecture Notes in Computer Science

Self Cite

125

106

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Abstract. This paper presents the evolving objects library (EOlib), an object-oriented framework for evolutionary computation (EC) that aims to provide a flexible set of classes to build EC applications. EOlib design objective is to be able to evolve any object in which fitness makes sense. In order to do so, EO concentrates on interfaces; any object can evolve if it is endowed with an interface to do so. In this paper, we describe what features an object must have in order to evolve, and some examples of how EO has been put to practice evolving neural networks, solutions to the Mastermind game, and other novel applications.

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Section: Applicationsmentioning

confidence: 95%

Evolving Objects: A General Purpose Evolutionary Computation Library

Keijzer¹,

Guervós

Romero

et al. 2002

Lecture Notes in Computer Science

Self Cite

125

106

View full text Add to dashboard Cite

show abstract

“…When played with P = 4 and N = 6, the algorithm needs an average of 4.64 trials and evaluates only 41.2 combinations on average, which is not even four percent of all 1296 possible combinations. Bernier et al (1996), Bento et al (1999) and Kalisker & Camens (2003) all propose a GA that uses a fitness value that reflects the eligibility. Bernier et al (1996) need an average of 5.62 guesses over 693 games when played with N = P = 6.…”

Section: Rules Of Thumbmentioning

confidence: 99%

“…Bernier et al (1996), Bento et al (1999) and Kalisker & Camens (2003) all propose a GA that uses a fitness value that reflects the eligibility. Bernier et al (1996) need an average of 5.62 guesses over 693 games when played with N = P = 6. Bento et al (1999) reach an average of 6.866 guesses when played with P = 5 and N = 8.…”

Section: Rules Of Thumbmentioning

confidence: 99%

Efficient solutions for Mastermind using genetic algorithms

Berghman¹,

Goossens²,

Leus³

2009

Computers & Operations Research

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We present a new genetic algorithm for playing the game of Mastermind. The algorithm requires low run-times and results in a low expected number of guesses. Its performance is comparable to that of other meta-heuristics for the standard setting with four positions and six colors, while it outperforms the existing algorithms when more colors and positions are examined. The central idea underlying the algorithm is the creation of a large set of eligible guesses collected throughout the different generations of the genetic algorithm, the quality of each of which is subsequently determined based on a comparison with a selection of elements of the set.

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“…the score, or length of time survived). As with all reinforcement learning problems, different methods can be used to solve the problem (find a good policy) [15] including TD-learning [16], evolutionary computation [11], competitive coevolution [17], [18], [19], [20], simulated annealing [21], other optimisation algorithms and a large number of combinations between such algorithms [22]. In recent years a large number of papers that describe the application of various learning methods to different types of video games have appeared in the literature (including several overviews [23], [11], [24], [25]).…”

Section: A Npc Behavior Learningmentioning

confidence: 99%

A Panorama of Artificial and Computational Intelligence in Games

Yannakakis

Togelius

2015

IEEE Trans. Comput. Intell. AI Games

132

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Abstract-This paper attempts to give a high-level overview of the field of artificial and computational intelligence (AI/CI) in games, with particular reference to how the different core research areas within this field inform and interact with each other, both actually and potentially. We identify ten main research areas within this field: NPC behavior learning, search and planning, player modeling, games as AI benchmarks, procedural content generation, computational narrative, believable agents, AI-assisted game design, general game artificial intelligence and AI in commercial games. We view and analyze the areas from three key perspectives: (1) the dominant AI method(s) used under each area; (2) the relation of each area with respect to the end (human) user; and (3) the placement of each area within a human-computer (player-game) interaction perspective. In addition, for each of these areas we consider how it could inform or interact with each of the other areas; in those cases where we find that meaningful interaction either exists or is possible, we describe the character of that interaction and provide references to published studies, if any. We believe that this paper improves understanding of the current nature of the game AI/CI research field and the interdependences between its core areas by providing a unifying overview. We also believe that the discussion of potential interactions between research areas provides a pointer to many interesting future research projects and unexplored subfields.

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Solving MasterMind using GAs and simulated annealing: A case of dynamic constraint optimization

Cited by 21 publications

References 7 publications

Evolving Objects: A General Purpose Evolutionary Computation Library

Evolving Objects: A General Purpose Evolutionary Computation Library

Efficient solutions for Mastermind using genetic algorithms

A Panorama of Artificial and Computational Intelligence in Games

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