Optimization Calculating of Artificial Intelligence IPGA Algorithm

The traditional genetic algorithm (GA) known as the island model parallel genetic algorithm (IPGA) has limitations regarding speed and accuracy, especially when applied to large-scale datasets in parallel computing environments. This study addresses the optimization challenges associated with IPGA in the context of big data. Initially, an enhanced adaptive covariance matrix evolution strategy (CMA-ES) is implemented to replace the conventional Gaussian evolution strategy used in traditional IPGA. Additionally, a normalization function is integrated into the CMA-ES framework to constrain the range of random values during the iterative optimization process, thereby improving both the speed and accuracy of IPGA in managing big data computations. Furthermore, the MapReduce paradigm is utilized within a Hadoop cluster to optimize the computational process of IPGA, making it more suitable for distributed parallel operations on extensive datasets. Experimental findings indicate that the proposed methodologies significantly enhance the speed and accuracy of IPGA optimization, particularly in the context of large-scale and ultra-large-scale datasets, with a marked improvement in computational speed.