To reduce computational complexity and enable real-time deployment of the generated approximate circuits, several optimizations can be considered. One approach is to refine the approximation candidate selection process by incorporating more sophisticated heuristics or machine learning techniques to efficiently prune the design space and focus on high-impact approximations. Additionally, optimizing the genetic algorithm parameters, such as population size and mutation rates, can improve convergence speed and solution quality. Implementing parallel processing techniques to leverage multi-core architectures can also enhance the scalability and speed of the optimization process. Furthermore, exploring hardware acceleration or dedicated hardware implementations for critical components of the framework can further reduce computation time and enable real-time deployment of the approximate circuits.