Year: 2025
Type: conference-paper
Abstract: Boosting algorithms, recognized for their ability to create strong learners from weak learners, are valuable assets in many areas.Often it is difficult to optimize their hyperparameters, especially when working with highdimensional datasets.Metaheuristic algorithms, based on natural and evolutionary processes, provide great alternatives to traditional optimization approaches to hyperparameter tuning for boosting models with efficient and robust solutions.This review paper will present an overview of the existing state-of-the-art metaheuristic algorithms utilized in boosting model optimization, highlighting a wide variety of metaheuristic algorithms.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411257507