1. Adelaide Research & Scholarship

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Results 71-80 of 100 (Search time: 0.002 seconds).
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PreviewIssue DateTitleAuthor(s)
2015Efficient optimization of many objectives by approximation-guided evolutionWagner, M.; Bringmann, K.; Friedrich, T.; Neumann, F.
2013A novel feature-based approach to characterize algorithm performance for the traveling salesperson problemMersmann, O.; Bischl, B.; Trautmann, H.; Wagner, M.; Bossek, J.; Neumann, F.
2015Analyzing the effects of instance features and algorithm parameters for max-min ant system and the traveling salesperson problemNallaperuma, S.; Wagner, M.; Neumann, F.
2023Online Deployment Algorithms for Microservice Systems with Complex DependenciesHe, X.; Tu, Z.; Wagner, M.; Xu, X.; Wang, Z.
2015On the performance of different genetic programming approaches for the SORTING problemWagner, M.; Neumann, F.; Urli, T.
2015Seeding the initial population of multi-objective evolutionary algorithms: A computational studyFriedrich, T.; Wagner, M.
2021Wind turbine power output prediction using a new hybrid neuro-evolutionary methodNeshat, M.; Nezhad, M.M.; Abbasnejad, E.; Mirjalili, S.; Groppi, D.; Heydari, A.; Tjernberg, L.B.; Astiaso Garcia, D.; Alexander, B.; Shi, Q.; Wagner, M.
2013A fast and effective local search algorithm for optimizing the placement of wind turbinesWagner, M.; Day, J.; Neumann, F.
2013Predicting the energy output of wind farms based on weather data: important variables and their correlationVladislavleva, E.; Friedrich, T.; Neumann, F.; Wagner, M.
2016Incorporating domain knowledge into the optimization of energy systemsMahbub, M.; Wagner, M.; Crema, L.

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