Performance Analysis of Genetic Algorithms, Monte Carlo Methods, and Markov Models for Cloud-Based Scientific Computing

Abstract

Cloud-based scientific computing necessitates effective computational models in order to improve execution time, accuracy, and resource consumption. To measure computational efficiency and scalability, this study compares the performance of Genetic Algorithms (GA), Monte Carlo Methods (MCM), and Markov Models (MM) in a cloud context. The study formalizes the mathematical foundations of each technique and evaluates its effectiveness using controlled simulations. A comparison analysis and ablation study show that hybrid models increase performance, with the Full Model obtaining the highest accuracy (94.47%) and the best resource
utilization (90.42%). The study found that combining several computational methodologies improves efficiency, decision-making, and adaptability for large-scale scientific computations in cloud environments.