Local interactions drive emergent collective behavior, which pervades biological and social complex systems. But uncovering the interactions that produce a desired behavior remains a core challenge. In this paper, we present EvoSOPS, an evolutionary framework that searches landscapes of stochastic distributed algorithms for those that achieve a mathematically specified target behavior. These algorithms govern self-organizing particle systems (SOPS) comprising individuals with no persistent memory and strictly local sensing and movement. For aggregation, phototaxing, and separation behaviors, EvoSOPS discovers algorithms that achieve 4.2-15.3% higher fitness than those from the existing "stochastic approach to SOPS" based on mathematical theory from statistical physics. EvoSOPS is also flexibly applied to new behaviors such as object coating where the stochastic approach would require bespoke, extensive analysis. Finally, we distill insights from the diverse, best-fitness genomes produced for aggregation across repeated EvoSOPS runs to demonstrate how EvoSOPS can bootstrap future theoretical investigations into SOPS algorithms for new behaviors.

翻译：局部相互作用驱动着涌现的集体行为，这种现象普遍存在于生物和社会复杂系统中。然而，揭示能够产生期望行为的相互作用仍然是一个核心挑战。本文提出了EvoSOPS，这是一个演化框架，用于在随机分布式算法的空间中搜索那些能够实现数学上指定的目标行为的算法。这些算法控制着自组织粒子系统，该系统由不具备持久记忆、且仅具有严格局部感知和移动能力的个体组成。针对聚集、趋光和分离行为，EvoSOPS发现的算法比基于统计物理学数学理论的现有“SOPS随机方法”所得到的算法，其适应度高出4.2%至15.3%。EvoSOPS也能灵活应用于新行为，例如物体包覆，而随机方法则需要定制化的、广泛的分析。最后，我们提炼了在多次EvoSOPS运行中为聚集行为产生的多样、高适应度基因组中的洞见，以展示EvoSOPS如何能够为未来针对新行为的SOPS算法理论研究提供引导。