Günther R. Raidl mainly focuses on Mathematical optimization, Integer programming, Metaheuristic, Variable neighborhood search and Linear programming. He regularly links together related areas like Algorithm in his Mathematical optimization studies. His biological study deals with issues like Local search, which deal with fields such as Parallel metaheuristic.
His Variable neighborhood search study incorporates themes from Memetic algorithm, Vehicle routing problem, Scheduling and Heuristic. His work on Branch and price as part of general Linear programming research is frequently linked to Overlay network, Subsequence and Production line, bridging the gap between disciplines. The various areas that he examines in his Evolutionary algorithm study include Evolutionary computation, Algorithm design and Spanning tree.
Mathematical optimization, Variable neighborhood search, Metaheuristic, Integer programming and Algorithm are his primary areas of study. Günther R. Raidl works mostly in the field of Mathematical optimization, limiting it down to topics relating to Minimum spanning tree and, in certain cases, Spanning tree, as a part of the same area of interest. The concepts of his Variable neighborhood search study are interwoven with issues in Memetic algorithm, Local search, Vehicle routing problem and Steiner tree problem.
The study incorporates disciplines such as Greedy randomized adaptive search procedure and Ant colony optimization algorithms in addition to Metaheuristic. His study in Integer programming is interdisciplinary in nature, drawing from both Column generation, Facility location problem, Solver and Network planning and design. His research in Algorithm intersects with topics in Genetic algorithm and Heuristic.
Günther R. Raidl focuses on Mathematical optimization, Integer programming, Heuristic, Algorithm and Beam search. The Mathematical optimization study combines topics in areas such as Scheduling and Vehicle routing problem. His Integer programming research also works with subjects such as
He has included themes like Theoretical computer science, Dial a ride, Public transport, Benchmark and Variable neighborhood search in his Heuristic study. His studies deal with areas such as Set cover problem and Heuristics as well as Algorithm. His Beam search research incorporates elements of Longest common subsequence problem and Local search.
Günther R. Raidl spends much of his time researching Mathematical optimization, Integer programming, Particle therapy, Heuristic and Heuristic. His Mathematical optimization research integrates issues from Scheduling and Vehicle routing problem. His study focuses on the intersection of Integer programming and fields such as Linear programming with connections in the field of Graph, Maximization and Data mining.
Günther R. Raidl has researched Heuristic in several fields, including Benders' decomposition, Boosting and Dial a ride, Public transport. His studies in Metaheuristic integrate themes in fields like Routing and Travelling salesman problem. His Optimization problem research is multidisciplinary, incorporating perspectives in Facility location problem, Job scheduler, Computation and Tardiness.
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Hybrid metaheuristics in combinatorial optimization: A survey
Christian Blum;Jakob Puchinger;Günther R. Raidl;Andrea Roli.
soft computing (2011)
Evolutionary Computation in Combinatorial Optimization
Jens Gottlieb;Günther R. Raidl.
Combining metaheuristics and exact algorithms in combinatorial optimization: a survey and classification
Jakob Puchinger;Günther R. Raidl.
international work conference on the interplay between natural and artificial computation (2005)
Edge sets: an effective evolutionary coding of spanning trees
G.R. Raidl;B.A. Julstrom.
IEEE Transactions on Evolutionary Computation (2003)
CyMATE: a new tool for methylation analysis of plant genomic DNA after bisulphite sequencing.
Jennifer Hetzl;Andrea M. Foerster;Günther Raidl;Ortrun Mittelsten Scheid.
Plant Journal (2007)
The Multidimensional Knapsack Problem: Structure and Algorithms
Jakob Puchinger;Günther R. Raidl;Ulrich Pferschy.
Informs Journal on Computing (2010)
A unified view on hybrid metaheuristics
Günther R. Raidl.
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics (2006)
An improved genetic algorithm for the multiconstrained 0-1 knapsack problem
ieee international conference on evolutionary computation (1998)
Models and algorithms for three-stage two-dimensional bin packing
Jakob Puchinger;Günther R. Raidl.
European Journal of Operational Research (2007)
Prüfer numbers: a poor representation of spanning trees for evolutionary search
Jens Gottlieb;Bryant A. Julstrom;Günther R. Raidl;Franz Rothlauf.
genetic and evolutionary computation conference (2001)
Profile was last updated on December 6th, 2021.
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