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.

翻译：本研究探讨了由一组针对特定能力优化的同质个体所涌现的集体行为。我们创建了一组基于简单、相同神经网络的智能体，其模型仿效了遵循信息素轨迹的趋化性驱动载体，并利用这些演化个体的克隆体进行了多智能体仿真。结果表明，个体的演化导致了种群分化。令人惊讶的是，我们观察到在演化后期阶段，尽管个体性能保持较高水平且神经结构简化，集体适应度却发生了显著变化。这种下降发生在智能体发展出减弱的传感-运动耦合时，表明对个体智能体的过度优化几乎总是导致群体行为效率降低。我们的研究探讨了个体分化如何通过何种演化路径得以形成。