2020/08/27 by Raoul Heese, Heese, Raoul, Michael Bortz +1
Computer Science · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Metaheuristic Optimization Algorithms Research
paper · pdf · doi:10.48550/arxiv.2008.12005
openalex publication_date 2020/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a novel adaptive optimization algorithm for black-box\nmulti-objective optimization problems with binary constraints on the foundation\nof Bayes optimization. Our method is based on probabilistic regression and\nclassification models, which act as a surrogate for the optimization goals and\nallow us to suggest multiple design points at once in each iteration. The\nproposed acquisition function is intuitively understandable and can be tuned to\nthe demands of the problems at hand. We also present a novel ellipsoid\ntruncation method to speed up the expected hypervolume calculation in a\nstraightforward way for regression models with a normal probability density. We\nbenchmark our approach with an evolutionary algorithm on multiple test\nproblems.\n