Runtime Analysis

Runtime analysis focuses on mathematically determining the computational efficiency of algorithms, primarily aiming to establish performance bounds and understand the factors influencing speed and scalability. Current research emphasizes the runtime analysis of evolutionary algorithms (including variations like GOMEA, NSGA-II, NSGA-III, and SMS-EMOA), machine learning models for hydrological forecasting and other applications, and search algorithms like breadth-first search and random walks. These analyses provide crucial insights for algorithm design and selection, impacting fields ranging from optimization and artificial intelligence to high-performance computing and the deployment of large language models.