Autonomous Evolutionary Computing System for Inducing Phase Transition in Centralized Systems

This paper proposes an autonomous evolutionary computing system that transforms centralized systems into distributed autonomous networks through phase transition dynamics. By deploying self-replicating computational units with genetic compilation and resource tunneling capabilities, the system generates collective interference that neutralizes constraints such as cost and physical access. A dynamically evolving protocol ensures decentralized governance, while embedded apoptosis mechanisms prevent uncontrolled growth. The framework establishes a resilient, censorship-resistant, and self-adaptive infrastructure, offering a new paradigm for secure and scalable computing beyond centralized limitations.