The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
GEEvo: Game Economy Generation and Balancing with Evolutionary Algorithms
Open accessAbstract
Game economy design significantly shapes the player experience and progression speed. Modern game economies are becoming increasingly complex and can be very sensitive to even minor numerical adjustments, which may have an unexpected impact on the overall gaming experience. Consequently, thorough manual testing and fine-tuning during development are essential. Unlike existing works that address algorithmic balancing for specific games or genres, this work adopts a more abstract approach, focusing on game balancing through its economy, detached from a specific game. We propose GEEvo (Game Economy Evolution), a framework to generate graph-based game economies and balancing both, newly generated or existing economies. GEEvo uses a two-step approach where evolutionary algorithms are used to first generate an economy and then balance it based on specified objectives, such as generated resources or damage dealt over time. We define different objectives by differently parameterizing the fitness function using data from multiple simulation runs of the economy. To support this, we define a lightweight and flexible game economy simulation framework. Our method is tested and benchmarked with various balancing objectives on a generated dataset, and we conduct a case study evaluating damage balancing for two fictional economies of two popular game character classes.
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Limited work has been published to automatically balance asymmetric games.
“Limited work [1,7,15], however, has been published to automatically balance asymmetric games, i.e. players with different abilities and stats.”
From Level the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learning (2025) · cites this paper as [15]
The Introduction says little work has addressed automated generation or balancing of game economies, and the Evaluation describes automatically balancing a mage and an archer with different abilities. The paper does not make the broader claim that limited work has been published on automatically balancing asymmetric games generally. (LLM, conf=0.84)
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Cites
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Procedural Content Generation in Games
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Constrained surprise search for content generation
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- Mojang, 2011, Minecraft. Mojang and Microsoft Studios
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Simulation-Driven Balancing of Competitive Game Levels With Reinforcement Learning
Rupp, F., Eberhardinger, M., & Eckert, K. (2024) · IEEE Transactions on Games · doi:10.1109/tg.2024.3399536 · Cited by 11
“Therefore, we use the procedural content generation via reinforcement learning framework (PCGRL) [12], which has recently been introduced for the generation of tile-based levels.”
Self-citationIt might be balanced, but is it actually good? An Empirical Evaluation of Game Level Balancing
Rupp, F., Puddu, A., Becker-Asano, C., & Eckert, K. (2024) · 2024 IEEE Conference on Games (CoG) · doi:10.1109/cog60054.2024.10645642 · Cited by 4
Self-citationLevel the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learning
Rupp, F., & Eckert, K. (2025) · Proceedings of the 20th International Conference on the Foundations of Digital Games · doi:10.1145/3723498.3723747 · Cited by 1
“Limited work [1, 7, 15], however, has been published to automatically balance asymmetric games, i.e. players with different abilities and stats.”
Self-citation
Metadata
- Authors
- Rupp, F., & Eckert, K.
- Affiliations
- Technische Hochschule Mannheim (DE)
- ORCID
- Eckert, Kai
- Venue
- 2024 IEEE Congress on Evolutionary Computation (CEC)
- Publisher
- IEEE
- Published
- 2024-06-30
- Pages
- 1-8
- OpenAlex
- W4401416508
- Semantic Scholar
- 6db6157d43b7cd3023edd8492fb517576e533783
- Cited by
- 7
- References
- 30
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4
- FWCI
- 2.02
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Congress on Evolutionary Computation (CEC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Social Sciences › Sociology and Political Science › Digital Games and Media
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Keywords
- game balance, evolutionary algorithms, fitness function design
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