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This paper presents a hybrid procedural content generation (PCG) framework that integrates Wave Function Collapse (WFC) for constraint-based structure synthesis with Genetic Algorithms (GA) for adaptive gameplay optimization. Experimental results in Unity show that the hybrid model improves level playability, structural consistency, and generation efficiency, achieving 56% faster convergence and 73% fewer unplayable levels compared to standalone methods. The approach demonstrates the potential of hybrid PCG techniques to enhance scalability, diversity, and player experience in game content generation.

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Hybrid Procedural Level Generation Using Wave Function Collapse and Genetic Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Anton Karpetskyi,
  • Piotr Napieralski,
  • Dominik Szajerman

摘要

This paper presents a hybrid procedural content generation (PCG) framework that integrates Wave Function Collapse (WFC) for constraint-based structure synthesis with Genetic Algorithms (GA) for adaptive gameplay optimization. Experimental results in Unity show that the hybrid model improves level playability, structural consistency, and generation efficiency, achieving 56% faster convergence and 73% fewer unplayable levels compared to standalone methods. The approach demonstrates the potential of hybrid PCG techniques to enhance scalability, diversity, and player experience in game content generation.