Günther Raidl
Scholar

Günther Raidl

Google Scholar ID: -vFfXIsAAAAJ
Professor, TU Wien
combinatorial optimizationmachine learning
Citations & Impact
All-time
Citations
5,970
 
H-index
27
 
i10-index
82
 
Publications
20
 
Co-authors
0
 
Publications
20 items
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
  • Editorial board member or associate editor for multiple international journals, including: Algorithms (MDPI, since 2020), Engineering Applications of Artificial Intelligence (Elsevier, since 2022), ACM Transactions on Evolutionary Learning and Optimization (since 2019), INFORMS Journal on Computing (since 2013), etc.
  • Associate editor of Evolutionary Computation Journal (MIT Press, 2005–2014); editorial board member since 2015
  • Long-standing editorial roles in journals on metaheuristics and AI applications
  • Co-founder and steering committee member of the EvoCOP conference series
  • Steering committee member of EvoAPPS since 2018
  • Co-Chair of the Workshop on Theory and Applications of Metaheuristic Algorithms at EUROCAST 2024
  • Local Chair of SoCS 2022 in Vienna
  • Program Chair of MIC 2013 in Singapore
  • Local Co-Organizer of EvoStar 2013 in Vienna
  • General and Local Chair of the Hybrid Metaheuristics Workshop in 2010
  • Track Co-Chair at GECCO 2011 in Dublin
  • Executive Board member of ÖGOR (Austrian Society of Operations Research, 2013–2016)
  • Management Board member of EU COST Action CA15140 ImAppNIO since 2016
Background
  • Research interests include algorithmic problems in application domains such as network design and other graph problems, transportation optimization, cutting and packing, computational biology, scheduling and timetabling
  • Focus on combinatorial optimization using exact methods (e.g., mathematical programming, branch and bound, dynamic programming, constraint programming) as well as heuristics and metaheuristics
  • Research in machine learning and reinforcement learning
  • Particular emphasis on hybrid optimization techniques combining (meta-)heuristics, exact optimization, and machine learning
  • Interest in efficient algorithms and data structures
Co-authors
0 total
Co-authors: 0 (list not available)