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，揭示产生目标行为的相互作用机制仍是一个核心挑战。本文提出EvoSOPS进化框架，该框架通过搜索随机分布式算法空间来发现能够实现数学定义目标行为的算法。这些算法控制着由无持久记忆、仅具备严格局部感知和移动能力的个体组成的自组织粒子系统（SOPS）。针对聚集、趋光性和分离行为，EvoSOPS发现的算法相比基于统计物理学数学理论的现有"SOPS随机方法"，适应度提升4.2-15.3%。EvoSOPS还能灵活应用于物体包覆等新行为，而随机方法对此类行为需进行特化且复杂的分析。最后，我们从多次EvoSOPS运行中产生的多样化最优适应度基因组中提炼洞见，论证了EvoSOPS如何能够引导未来关于面向新行为的SOPS算法的理论研究。