2017/04/19 by Simon Wessing, Wessing, Simon, Mike Preuss +2 · 1 citation
Computer Science · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Advanced Optimization Algorithms Research #FOS: Computer and information sciences #FOS: Mathematics #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Optimization and Control (math.OC) #cs.NE #math.OC
paper · pdf · doi:10.48550/arxiv.1704.05724
arxiv created 2017/04/19 · openalex publication_date 2017/04/19 · arxiv updated 2017/04/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Efficient global optimization is a popular algorithm for the optimization of expensive multimodal black-box functions. One important reason for its popularity is its theoretical foundation of global convergence. However, as the budgets in expensive optimization are very small, the asymptotic properties only play a minor role and the algorithm sometimes comes off badly in experimental comparisons. Many alternative variants have therefore been proposed over the years. In this work, we show experimentally that the algorithm instead has its strength in a setting where multiple optima are to be identified.