  Urban growth sometimes leads to rigid infrastructure that struggles to adapt
to changing demand. This paper introduces a novel approach, aiming to enable
cities to evolve and respond more effectively to such dynamic demand. It
identifies the limitations arising from the complexity and inflexibility of
existing urban systems. A framework is presented for enhancing the city's
adaptability perception through advanced sensing technologies, conducting
parallel simulation via graph-based techniques, and facilitating autonomous
decision-making across domains through decentralized and autonomous
organization and operation. Notably, a symbiotic mechanism is employed to
implement these technologies practically, thereby making urban management more
agile and responsive. In the case study, we explore how this approach can
optimize traffic flow by adjusting lane allocations. This case not only
enhances traffic efficiency but also reduces emissions. The proposed
evolutionary city offers a new perspective on sustainable urban development,
highliting the importance of integrated intelligence within urban systems.
