Learning Beam Search: Utilizing Machine Learning to Guide Beam Search for Solving Combinatorial Optimization Problems. - Marc Huber; Günther R. Raidl (LOD, 2021)

A Learning Large Neighborhood Search for the Staff Rerostering Problem. - Fabio F. Oberweger; Günther R. Raidl; Elina Rönnberg; Marc Huber (CPAIOR, 2022)

A Relative Value Function Based Learning Beam Search for the Longest Common Subsequence Problem. - Marc Huber; Günther R. Raidl (EUROCAST, 2022)

A Beam Search for the Shortest Common Supersequence Problem Guided by an Approximate Expected Length Calculation. - Jonas Mayerhofer; Markus Kirchweger; Marc Huber; Günther R. Raidl (EvoCOP, 2022)

A Policy-Based Learning Beam Search for Combinatorial Optimization. - Rupert Ettrich; Marc Huber; Günther R. Raidl (EvoCOP, 2023)

Learning to Select Promising Initial Solutions for Large Neighborhood Search-Based Multi-Agent Path Finding. - Marc Huber; Günther R. Raidl; Christian Blum 001 (EUROCAST (1), 2024)