This article presents genetic wave function collapse (WFC), a procedural level generation algorithm that mixes genetic optimization with WFC, a local adjacency constraints propagation algorithm. We use a synthetic player to evaluate the novelty, safety, and complexity of the generated levels. Novelty is maximized when the synthetic player goes on tiles not visited for a long time, safety is related to how far it can see, and complexity evaluates the variability of the surrounding tiles. WFC extracts constraints from example levels, and allows us to perform the genetic search on levels with few local asset placement errors, while using as little level design rules as possible. We show that we are able to rely on WFC while optimizing the results, first by influencing WFC asset selection and then by reencoding the chosen modules back to our genotype, in order to optimize crossover. We compare the fitness curves and best maps of our method with other approaches. We then visually explore the kind of levels we are able to generate by sampling different values of safety and complexity, giving a glimpse of the variability that our approach is able to reach.

中文翻译：

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Genetic-WFC：通过遗传搜索扩展波函数坍缩

本文介绍了遗传波函数崩溃 (WFC)，这是一种将遗传优化与 WFC（一种局部邻接约束传播算法）相结合的过程级生成算法。我们使用合成播放器来评估生成关卡的新颖性、安全性和复杂性。当合成玩家进入长时间未访问的瓷砖时，新颖性最大化，安全性与它能看到多远有关，复杂性评估周围瓷砖的可变性。WFC 从示例关卡中提取约束，并允许我们在几乎没有本地资产放置错误的关卡上执行遗传搜索，同时使用尽可能少的关卡设计规则。我们表明我们能够在优化结果时依赖 WFC，首先通过影响 WFC 资产选择，然后通过将所选模块重新编码回我们的基因型，为了优化交叉。我们将我们方法的适应度曲线和最佳地图与其他方法进行比较。然后，我们通过对不同的安全性和复杂性值进行采样，直观地探索我们能够生成的级别类型，从而一瞥我们的方法能够达到的可变性。