H-Index & Metrics Top Publications
Günther R. Raidl

Günther R. Raidl

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 32 Citations 6,400 160 World Ranking 7022 National Ranking 62
Mathematics H-index 33 Citations 6,031 157 World Ranking 1632 National Ranking 23

Overview

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Hybrid metaheuristics in combinatorial optimization: A survey

Christian Blum;Jakob Puchinger;Günther R. Raidl;Andrea Roli.
soft computing (2011)

780 Citations

Evolutionary Computation in Combinatorial Optimization

Jens Gottlieb;Günther R. Raidl.
(2004)

465 Citations

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)

437 Citations

Edge sets: an effective evolutionary coding of spanning trees

G.R. Raidl;B.A. Julstrom.
IEEE Transactions on Evolutionary Computation (2003)

365 Citations

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)

202 Citations

The Multidimensional Knapsack Problem: Structure and Algorithms

Jakob Puchinger;Günther R. Raidl;Ulrich Pferschy.
Informs Journal on Computing (2010)

193 Citations

A unified view on hybrid metaheuristics

Günther R. Raidl.
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics (2006)

180 Citations

An improved genetic algorithm for the multiconstrained 0-1 knapsack problem

G.R. Raidl.
ieee international conference on evolutionary computation (1998)

165 Citations

Models and algorithms for three-stage two-dimensional bin packing

Jakob Puchinger;Günther R. Raidl.
European Journal of Operational Research (2007)

156 Citations

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)

156 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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