Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Exploring A Two-market Genetic Algorithm
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
GameFlow: a model for evaluating player enjoyment in games
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
A Theory of Fun for Game Design
A Theory of Fun for Game Design
Rules of Play: Game Design Fundamentals
Rules of Play: Game Design Fundamentals
Rhythm-based level generation for 2D platformers
Proceedings of the 4th International Conference on Foundations of Digital Games
Modeling player experience in super mario bros
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Towards capturing and enhancing entertainment in computer games
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Towards multiobjective procedural map generation
Proceedings of the 2010 Workshop on Procedural Content Generation in Games
Cellular automata for real-time generation of infinite cave levels
Proceedings of the 2010 Workshop on Procedural Content Generation in Games
Interactive evolution for the procedural generation of tracks in a high-end racing game
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Procedural content generation for games: A survey
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Controllable procedural map generation via multiobjective evolution
Genetic Programming and Evolvable Machines
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This paper presents a generative system for the automatic creation of video game levels. Our approach is novel in that it allows high-level design goals to be expressed in a top-down manner, while existing bottom-up techniques do not. We use the FI-2Pop genetic algorithm as a natural way to express both constraints and optimization goals for potential level designs. We develop a genetic encoding technique specific to level design, which proves to be extremely flexible. Example levels are generated for two different genres of game, demonstrating the system’s broad applicability.