个体越优化，群体越失效：神经网络小车实验揭示过度优化反噬集体表现
Evolution of Collective AI Beyond Individual Optimization
- 用相同神经网络代理模拟趋化行为，观察群体演化
- 个体性能保持高位，群体适应度却在后期显著下降
- 个体感官与运动耦合减弱是导致群体效率降低的关键
本研究探究了同质个体在特定能力优化后所涌现的集体行为。我们构建了一组基于神经网络的简单、相同的代理，其行为类比趋化驱动的车辆，沿信息素轨迹移动，并对这些进化个体的克隆体进行多智能体模拟。结果表明，个体的进化导致了种群分化。令人惊讶的是，在进化后期，尽管个体表现持续优异且神经结构趋于简化，集体适应度却显著下降。该现象发生在代理出现感知-运动耦合减弱时，暗示对个体的过度优化几乎总会损害群体效能。研究进一步探讨了个体分化通过何种进化路径形成。
原文摘要 · Abstract (English)
This study investigates collective behaviors that emerge from a group of homogeneous individuals optimized for a specific capability. We created a group of simple, identical neural network based agents modeled after chemotaxis-driven vehicles that follow pheromone trails and examined multi-agent simulations using clones of these evolved individuals. Our results show that the evolution of individuals led to population differentiation. Surprisingly, we observed that collective fitness significantly changed during later evolutionary stages, despite maintained high individual performance and simplified neural architectures. This decline occurred when agents developed reduced sensor-motor coupling, suggesting that over-optimization of individual agents almost always lead to less effective group behavior. Our research investigates how individual differentiation can evolve through what evolutionary pathways.
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