The study explores the spontaneous evolution of modularity and network motifs in biological networks, which are characterized by modular structures and recurring patterns called network motifs. Traditional evolutionary models often produce nonmodular and complex networks, lacking the simplicity and modularity observed in biological systems. The authors use evolutionary algorithms to evolve networks under an environment that changes in a modular fashion, switching between different goals with varying subgoals. This approach leads to the spontaneous evolution of modular network structures and network motifs. The evolved networks rapidly adapt to each new goal, demonstrating the ability to retain and reuse substructures across different tasks. The study suggests that modularly varying goals can guide evolution towards more modular and efficient designs, potentially explaining the evolutionary forces that promote structural simplicity in biological networks. This finding has implications for improving the evolutionary design of engineered systems.The study explores the spontaneous evolution of modularity and network motifs in biological networks, which are characterized by modular structures and recurring patterns called network motifs. Traditional evolutionary models often produce nonmodular and complex networks, lacking the simplicity and modularity observed in biological systems. The authors use evolutionary algorithms to evolve networks under an environment that changes in a modular fashion, switching between different goals with varying subgoals. This approach leads to the spontaneous evolution of modular network structures and network motifs. The evolved networks rapidly adapt to each new goal, demonstrating the ability to retain and reuse substructures across different tasks. The study suggests that modularly varying goals can guide evolution towards more modular and efficient designs, potentially explaining the evolutionary forces that promote structural simplicity in biological networks. This finding has implications for improving the evolutionary design of engineered systems.