用进化算法优化WFC的输入样本，提升生成内容质量。
Evolutionary Wave Function Collapse
- 通过进化搜索调整WFC的输入样例，而非直接优化完整地图。
- 在迷宫连通性和地牢布局中，生成质量显著提升。
- 适合局部结构决定整体特征的生成任务，如游戏关卡设计。
Wave Function Collapse（WFC）是一种广泛使用的程序化内容生成方法，通过从示例输入中学习局部邻接约束来生成更大输出。本文探索将WFC与进化搜索结合：不是直接进化完整关卡，而是进化WFC所用的小规模输入示例。在此框架下，WFC充当基因型到表型的映射，生成的地图通过领域特定的适应度函数评估。我们在两个不同领域进行测试：迷宫连通性地图和塞尔达风格地牢布局。结果表明，在局部关系决定全局属性的场景中，对WFC输入的进化优化能有效提升生成质量；而需要全局约束的场景仍具挑战性。这说明当目标与局部结构一致时，进化搜索可有效引导WFC生成。
原文摘要 · Abstract (English)
Wave Function Collapse (WFC) is a widely used procedural content generation method that learns local adjacency constraints from example inputs to generate larger outputs. In this paper, we explore combining WFC with evolutionary search by evolving the small input examples used by WFC rather than directly evolving complete levels. In this approach, WFC acts as a genotype-to-phenotype mapping. The generated levels are then evaluated through domain-specific fitness functions. We evaluate the method in two domains with different relationships between local and global structure: Maze connectivity maps and Zelda-style dungeon layouts. Our results show that evolutionary optimization over WFC inputs improves generation quality in domains where properties emerge from local relationships, while domains requiring global constraints remain challenging. These findings suggest that evolutionary search can effectively guide WFC generation when target objectives align with local structure.
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