2024/12/20 by Tobias Glasmachers, Glasmachers, Tobias
Computer Science · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.2412.15647
openalex publication_date 2024/12/20 · openalex created_date 2024/12/24 · openalex updated_date 2026/07/28
We design a class of variable metric evolution strategies well suited for high-dimensional problems. We target problems with many variables, not (necessarily) with many objectives. The construction combines two independent developments: efficient algorithms for scaling covariance matrix adaptation to high dimensions, and evolution strategies for multi-objective optimization. In order to design a specific instance of the class we first develop a (1+1) version of the limited memory matrix adaptation evolution strategy and then use an established standard construction to turn a population thereof into a state-of-the-art multi-objective optimizer with indicator-based selection. The method compares favorably to adaptation of the full covariance matrix.