What is he best known for?
The fields of study he is best known for:
- Algorithm
- Mathematical optimization
- Artificial intelligence
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.
His most cited work include:
- Hybrid metaheuristics in combinatorial optimization: A survey (467 citations)
- Evolutionary Computation in Combinatorial Optimization (300 citations)
- Combining metaheuristics and exact algorithms in combinatorial optimization: a survey and classification (247 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Mathematical optimization (59.00%)
- Variable neighborhood search (24.52%)
- Metaheuristic (22.61%)
What were the highlights of his more recent work (between 2016-2021)?
- Mathematical optimization (59.00%)
- Integer programming (19.16%)
- Heuristic (8.05%)
In recent papers he was focusing on the following fields of study:
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
- Combinatorial optimization problem which connect with Memetic algorithm, Computational intelligence and Cardinality,
- Iterative method together with Reduced cost, Iterative refinement and Minimum spanning tree.
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.
Between 2016 and 2021, his most popular works were:
- Full-load route planning for balancing bike sharing systems by logic-based benders decomposition (14 citations)
- Solving a selective dial-a-ride problem with logic-based Benders decomposition (12 citations)
- Solving the two-stage fixed-charge transportation problem with a hybrid genetic algorithm (10 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Artificial intelligence
- Mathematical optimization
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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