Automated algorithm selection promises to support the user in the decisive task of selecting a most suitable algorithm for a given problem. A common component of these machine-trained techniques are regression models which predict the performance of a given algorithm on a previously unseen problem instance. In the context of numerical black-box optimization, such regression models typically build on exploratory landscape analysis (ELA), which quantifies several characteristics of the problem. These measures can be used to train a supervised performance regression model.
First steps towards ELA-based performance regression have been made in the context of a fixed-target setting. In many applications, however, the user needs to select an algorithm that performs best within a given budget of function evaluations. Adopting this fixed-budget setting, we demonstrate that it is possible to achieve high-quality performance predictions with off-the-shelf supervised learning approaches, by suitably combining two differently trained regression models. We test this approach on a very challenging problem: algorithm selection on a portfolio of very similar algorithms, which we choose from the family of modular CMA-ES algorithms.
The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants
Jožef Stefan Institute（约泽夫·斯蒂芬研究所）；Jožef Stefan International Postgraduate School（约泽夫·斯蒂芬国际研究生院）；Leiden Institute for Advanced Computer Science（莱顿高级计算机科学研究所）；Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）
Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating
State Key Laboratory of Internet of Things for Smart City, University of Macau（澳门大学物联网智慧城市建设国家重点实验室）；College of Water Conservancy and Hydropower Engineering, Hohai University（河海大学水利水电学院）；School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）
Optimization Strategies for Variational Quantum Algorithms in Noisy Landscapes
VSB-Technical University of Ostrava（俄斯特拉发理工大学）；IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava（IT4创新国家超算中心，俄斯特拉发理工大学）；Klaipeda University（克莱佩达大学）
Does Dimensionality Reduction via Random Projections Preserve Landscape Features?
Leiden Institute of Advanced Computer Science, Leiden University（莱顿高级计算机科学研究所，莱顿大学）；RTWH Aachen University（亚琛工业大学）；Institute for Clarity in Documentation（文档清晰研究所）；Inria Paris-Rocquencourt（巴黎-罗quentourt研究所）；Rajiv Gandhi University（拉贾·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒研究实验室）
Robust Differential Evolution via Nonlinear Population Size Reduction and Adaptive Restart: The ARRDE Algorithm
Forschungszentrum Jülich GmbH（于利希研究中心）；The University of Osaka（大阪大学）；National Research and Innovation Agency, Indonesia（印度尼西亚国家研究与创新署）；Universitas Negeri Jakarta（雅加达国立大学）
OPAL: Operator-Programmed Algorithms for Landscape-Aware Black-Box Optimization
McCormick School of Engineering, Northwestern University（麦科姆ick工程学院，西北大学）；College of Artificial Intelligence, Nankai University（南开大学人工智能学院）；School of Mathematics, University of Science and Technology of China（中国科学技术大学数学系）；Guizhou University（贵州大学）；School of New Energy, Jingchu University of Technology（荆楚科技学院新能源学院）；Information Initiative Center, Hokkaido University（北海道大学信息初始化中心）；School of Computer Science and Artificial Intelligence, Wenzhou University（温州大学计算机科学与人工智能学院）
BBOPlace-Bench: Benchmarking Black-Box Optimization for Chip Placement
National Key Laboratory for Novel Software Technology（国家新型软件技术重点实验室）；School of Artificial Intelligence, Nanjing University（人工智能学院，南京大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）
