Exploring Evolutionary Algorithms for Optimizing Big Data Models: A Comparative Study

Abstract

This paper presents a comparative study of the effectiveness of various evolutionary algorithms in optimizing models for Big Data analytics. The study focuses on three widely used algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). A synthetic Big Data dataset, simulating real-world scenarios with various complexities, was used to train and optimize different models, including decision trees, support vector machines, and neural networks. The performance of these models was evaluated based on accuracy, computational efficiency, and convergence rate. The results demonstrate that evolutionary algorithms significantly enhance the optimization process, with each algorithm offering unique strengths and limitations. The study provides valuable insights into the practical application of these algorithms in Big Data analytics and highlights the need for further research in developing hybrid optimization techniques.