Evolutionary Computation (EC) techniques, including Genetic Algorithms, Evolution Strategies, and Genetic Programming, have long demonstrated strong performance in solving complex, non-convex optimization problems; however, despite their inherent parallelism, their deployment at exascale supercomputing levels remains relatively underexplored. In this paper, we present a comprehensive study of EC applications on modern supercomputing architectures, emphasizing massively parallel and hybrid implementations, and propose a scalable framework that leverages heterogeneous computing resources by integrating multi-core CPUs and GPUs to accelerate evolutionary processes. The framework is evaluated on a suite of large-scale, real-world optimization problems, including the Traveling Salesman Problem, hyperparameter optimization for deep neural networks, and neural architecture search, with experimental results demonstrating significant improvements in scalability, convergence speed, and solution quality compared to traditional implementations.
