This paper presents a cooperative coevolutionary approach to function optimization, comparing it with traditional genetic algorithms (GAs). The proposed method, called cooperative coevolutionary genetic algorithms (CCGAs), models the coevolution of cooperating species to solve complex problems. The approach is tested on function optimization, where it outperforms traditional GAs in both performance and convergence speed. The key idea is to decompose the problem into subcomponents, each managed by a separate subpopulation. The fitness of each subpopulation member is determined by its contribution to the overall solution. The CCGA-1 model is introduced, which initializes separate populations for each function variable and evolves them in a round-robin fashion. The results show that CCGA-1 performs significantly better than the standard GA on several multimodal functions, including Rastrigin, Schwefel, Griewangk, and Ackley. However, it performs worse on the Rosenbrock function, which has strong variable interactions. A modified version, CCGA-2, improves performance on the Rosenbrock function but slightly decreases performance on non-interacting problems. The paper concludes that cooperative coevolutionary systems have potential for solving complex problems, including the evolution of neural networks and rule sets. Future work includes exploring parallel implementations and applying the approach to more complex domains.This paper presents a cooperative coevolutionary approach to function optimization, comparing it with traditional genetic algorithms (GAs). The proposed method, called cooperative coevolutionary genetic algorithms (CCGAs), models the coevolution of cooperating species to solve complex problems. The approach is tested on function optimization, where it outperforms traditional GAs in both performance and convergence speed. The key idea is to decompose the problem into subcomponents, each managed by a separate subpopulation. The fitness of each subpopulation member is determined by its contribution to the overall solution. The CCGA-1 model is introduced, which initializes separate populations for each function variable and evolves them in a round-robin fashion. The results show that CCGA-1 performs significantly better than the standard GA on several multimodal functions, including Rastrigin, Schwefel, Griewangk, and Ackley. However, it performs worse on the Rosenbrock function, which has strong variable interactions. A modified version, CCGA-2, improves performance on the Rosenbrock function but slightly decreases performance on non-interacting problems. The paper concludes that cooperative coevolutionary systems have potential for solving complex problems, including the evolution of neural networks and rule sets. Future work includes exploring parallel implementations and applying the approach to more complex domains.