论文标题
通过图神经网络和宏观麦克罗演化提取集体行为的符号模型
Extracting Symbolic Models of Collective Behaviors with Graph Neural Networks and Macro-Micro Evolution
论文作者
论文摘要
集体行为通常很难建模。群体的规模，大量相互作用以及行为的丰富性和复杂性是使集体行为很难提炼成简单的符号表达的因素。在本文中，我们提出了一种新颖的方法，用于旨在促进这种建模的符号回归。使用原始和后处理的数据作为输入，我们的方法会产生可行的符号表达式，以密切对目标行为进行密切建模。我们的方法由两个阶段组成。首先，训练图形神经网络（GNN）以提取目标行为的近似值。在第二阶段，GNN用于生成一种称为宏观微膜进化（MME）的嵌套进化算法的数据。该算法的宏层选择候选符号表达式，而微观层则调谐其参数。实验评估表明，我们的方法在符号回归方面的表现优于竞争解决方案，这使得可以提取复杂群体行为的紧凑表达。
Collective behaviors are typically hard to model. The scale of the swarm, the large number of interactions, and the richness and complexity of the behaviors are factors that make it difficult to distill a collective behavior into simple symbolic expressions. In this paper, we propose a novel approach to symbolic regression designed to facilitate such modeling. Using raw and post-processed data as an input, our approach produces viable symbolic expressions that closely model the target behavior. Our approach is composed of two phases. In the first, a graph neural network (GNN) is trained to extract an approximation of the target behavior. In the second phase, the GNN is used to produce data for a nested evolutionary algorithm called macro-micro evolution (MME). The macro layer of this algorithm selects candidate symbolic expressions, while the micro layer tunes its parameters. Experimental evaluation shows that our approach outperforms competing solutions for symbolic regression, making it possible to extract compact expressions for complex swarm behaviors.
