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Markus Wagner
Markus Wagner
Associate Professor of Computer Science, Monash University, Australia
Zweryfikowany adres z monash.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Evolutionary many-objective optimization: A quick-start guide
S Chand, M Wagner
Surveys in Operations Research and Management Science 20 (2), 35-42, 2015
1692015
A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm
M Neshat, MM Nezhad, E Abbasnejad, S Mirjalili, LB Tjernberg, ...
Energy conversion and management 236, 114002, 2021
1492021
A comprehensive benchmark set and heuristics for the traveling thief problem
S Polyakovskiy, MR Bonyadi, M Wagner, Z Michalewicz, F Neumann
Proceedings of the 2014 annual conference on genetic and evolutionary …, 2014
1282014
A Fast and Effective Local Search Algorithm for Optimizing the Placement of Wind Turbines
M Wagner, J Day, F Neumann
Renewable Energy 51, 64-70, 2013
1272013
Benchmarking in optimization: Best practice and open issues
T Bartz-Beielstein, C Doerr, D Berg, J Bossek, S Chandrasekaran, ...
arXiv preprint arXiv:2007.03488, 2020
1182020
Development of underground mine monitoring and communication system integrated ZigBee and GIS
MA Moridi, Y Kawamura, M Sharifzadeh, EK Chanda, M Wagner, H Jang, ...
International Journal of Mining Science and Technology 25 (5), 811-818, 2015
1172015
Approximation-guided evolutionary multi-objective optimization
K Bringmann, T Friedrich, F Neumann, M Wagner
IJCAI Proceedings-International Joint Conference on Artificial Intelligence …, 2011
1032011
Predicting the energy output of wind farms based on weather data: Important variables and their correlation
E Vladislavleva, T Friedrich, F Neumann, M Wagner
Renewable Energy 50, 236-243, 2013
1022013
On the use of genetic programming to evolve priority rules for resource constrained project scheduling problems
S Chand, Q Huynh, H Singh, T Ray, M Wagner
Information Sciences 432, 146-163, 2018
972018
A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem
O Mersmann, B Bischl, H Trautmann, M Wagner, J Bossek, F Neumann
Annals of Mathematics and Artificial Intelligence 69, 151-182, 2013
962013
A fast approximation-guided evolutionary multi-objective algorithm
M Wagner, F Neumann
Proceedings of the 15th annual conference on Genetic and evolutionary …, 2013
862013
A case study of algorithm selection for the traveling thief problem
M Wagner, M Lindauer, M Mısır, S Nallaperuma, F Hutter
Journal of Heuristics 24, 295-320, 2018
802018
Performance analysis of ZigBee network topologies for underground space monitoring and communication systems
MA Moridi, Y Kawamura, M Sharifzadeh, EK Chanda, M Wagner, ...
Tunnelling and Underground Space Technology 71, 201-209, 2018
802018
Wind turbine power output prediction using a new hybrid neuro-evolutionary method
M Neshat, MM Nezhad, E Abbasnejad, S Mirjalili, D Groppi, A Heydari, ...
Energy 229, 120617, 2021
772021
Approximate approaches to the traveling thief problem
H Faulkner, S Polyakovskiy, T Schultz, M Wagner
Proceedings of the 2015 annual conference on genetic and evolutionary …, 2015
692015
Rosita: Towards automatic elimination of power-analysis leakage in ciphers
MA Shelton, N Samwel, L Batina, F Regazzoni, M Wagner, Y Yarom
arXiv preprint arXiv:1912.05183, 2019
682019
Faster black-box algorithms through higher arity operators
B Doerr, D Johannsen, T Kötzing, PK Lehre, M Wagner, C Winzen
Proceedings of the 11th workshop proceedings on Foundations of genetic …, 2011
672011
Optimizing the layout of 1000 wind turbines
M Wagner, K Veeramachaneni, F Neumann, UM O’Reilly
European wind energy association annual event 205209, 2011
662011
A hybrid cooperative co-evolution algorithm framework for optimising power take off and placements of wave energy converters
M Neshat, B Alexander, M Wagner
Information Sciences 534, 218-244, 2020
632020
Metaheuristics “in the large”
J Swan, S Adriaensen, AEI Brownlee, K Hammond, CG Johnson, A Kheiri, ...
European Journal of Operational Research 297 (2), 393-406, 2022
612022
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