Automated algorithm selection on continuous black-box problems by combining Exploratory Landscape Analysis and machine learning. Evol. Comput., (27)1:99--127, MIT Press, 2019. [PUMA: learning; single-objective analysis; black-box exploratory optimization; continuous machine optimization. Automated selection; algorithm landscape]
Automated algorithm selection: Survey and perspectives. Evol. Comput., (27)1:3--45, MIT Press, 2019. [PUMA: feature-based learning; metalearning data analysis; optimisation; automated streams.; exploratory combinatorial continuous machine approaches; Automated selection; algorithm configuration; landscape]
Dancing to the State of the Art?: How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem. In Michael Affenzeller, Stephan M. Winkler, Anna V. Kononova, Thomas Bäck, Heike Trautmann, Tea Tusar, und Penousal Machado (Hrsg.), Parallel Problem Solving from Nature – PPSN XVIII, 100--115, Springer, Berlin u. a., 07.09.2024. [PUMA: FIS_scads Hardness, Traveling Configuration, Benchmarking, Search, Problem topic_engineering Algorithm Heuristic Salesperson]
Efficient Dependency Analysis for Rule-Based Ontologies. In Ulrike Sattler, Aidan Hogan, Maria Keet, Valentina Presutti, João Paulo A. Almeida, Hideaki Takeda, Pierre Monnin, Giuseppe Pirrò, und Claudia d’Amato (Hrsg.), The Semantic Web – ISWC 2022, 267 -- 283, Springer, Berlin u. a., Germany, 16.10.2022. [PUMA: FIS_scads Acyclicity, Ontology-based Ontology Existential query stratification, Chase reasoning, topic_knowledge topic_graph dependencies, Rule answering, Core algorithm rules,]
Inverse Dirichlet weighting enables reliable training of physics informed neural networks. Machine learning: science and technology, (3)1IOP Publishing Ltd., 15.02.2022. [PUMA: FIS_scads training, modeling, neural multi-scale ALGORITHM active physics-informed forgetting, networks, regularization, gradient topic_lifescience catastrophic turbulence, flow multi-objective]