Evolutionary Generation of Game Levels

Connor, AM
Greig, TJ
Kruse, J
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Journal Article
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Journal Title
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Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (ICST)

This paper outlines an approach for evolutionary procedural generation of video game content. The study deals with the automatic generation of game level designs using genetic algorithms and the development of a fitness function that describes the playability of the game level. The research explores whether genetic algorithms have the ability to produce outcomes that demonstrate characteristics that arise through human creativity, and whether these automated approaches offer any benefits in terms of time and effort involved in the design process. The approach is compared to a random method and the results show that the genetic algorithm is more consistent in finding levels; however analysis of the game levels indicates that the fitness function is not fully capturing level playability. The ability to produce playable levels decreases as the play area increases, however there is potential to produce larger maps that are both playable and arguably creative through a recombination method.

Procedural Content Generation; Creative Computing; Novelty Generation; Video Game Design; Genetic Algorithms; Computational Creativity
EAI Endorsed Transactions on Creative Technologies, ct 18(15): e4, doi: 10.4108/eai.10-4-2018.155857
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Copyright © 2018 A.M. Connor et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.