Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
SOKOBAN and other motion planning problems
Computational Geometry: Theory and Applications
Sokoban: improving the search with relevance cuts
Theoretical Computer Science
Sokoban: enhancing general single-agent search methods using domain knowledge
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Zen and the Art of Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Multi-agent Planning in Sokoban
CEEMAS '07 Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V
Finding optimal solutions to Rubik's cube using pattern databases
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Zen Puzzle Garden is NP-complete
Information Processing Letters
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In this paper we present a novel genetic algorithm (GA) solution to a simple yet challenging commercial puzzle game known as Zen Puzzle Garden (ZPG). We describe the game in detail, before presenting a suitable encoding scheme and fitness function for candidate solutions. By constructing a simulator for the game, we compare the performance of the GA with that of the A* algorithm. We show that the GA is competitive with informed search in terms of solution quality, and significantly out-performs it in terms of computational resource requirements. By highlighting relevant features of the game we hope to stimulate further work on its study, and we conclude by presenting several possible areas for future research.