D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 35 Citations 6,937 169 World Ranking 5784 National Ranking 274

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Algorithm
  • Gene

His primary areas of study are Mathematical optimization, Ant colony optimization algorithms, Artificial intelligence, Algorithm and Metaheuristic. Tardiness is closely connected to Pheromone in his research, which is encompassed under the umbrella topic of Mathematical optimization. His study explores the link between Ant colony optimization algorithms and topics such as Scheduling that cross with problems in Heuristics.

His Algorithm study incorporates themes from RNA and ANT. In his study, Parallel metaheuristic, Artificial bee colony algorithm, Bees algorithm and Selection is inextricably linked to Swarm intelligence, which falls within the broad field of Metaheuristic. His Molecular evolution study, which is part of a larger body of work in Genome, is frequently linked to Pipeline, bridging the gap between disciplines.

His most cited work include:

  • MITOS: Improved de novo metazoan mitochondrial genome annotation (2019 citations)
  • Ant colony optimization for resource-constrained project scheduling (609 citations)
  • A hierarchical particle swarm optimizer and its adaptive variant (310 citations)

What are the main themes of his work throughout his whole career to date?

Martin Middendorf spends much of his time researching Mathematical optimization, Ant colony optimization algorithms, Artificial intelligence, Metaheuristic and Algorithm. Much of his study explores Mathematical optimization relationship to Benchmark. His Ant colony optimization algorithms research is multidisciplinary, incorporating elements of Pheromone, Tardiness, Heuristics, Population based and Scheduling.

His study in Artificial intelligence is interdisciplinary in nature, drawing from both Swarm intelligence, Machine learning and ANT. The various areas that Martin Middendorf examines in his Metaheuristic study include Multi-swarm optimization and Parallel computing. His research on Algorithm frequently connects to adjacent areas such as Gene orders.

He most often published in these fields:

  • Mathematical optimization (22.86%)
  • Ant colony optimization algorithms (22.04%)
  • Artificial intelligence (18.37%)

What were the highlights of his more recent work (between 2012-2021)?

  • Mathematical optimization (22.86%)
  • Ant colony optimization algorithms (22.04%)
  • Genome (8.16%)

In recent papers he was focusing on the following fields of study:

His main research concerns Mathematical optimization, Ant colony optimization algorithms, Genome, Algorithm and Metaheuristic. His work carried out in the field of Ant colony optimization algorithms brings together such families of science as Heuristics, Flow shop scheduling and Pattern recognition. His research investigates the link between Heuristics and topics such as Random search that cross with problems in Artificial intelligence.

Martin Middendorf interconnects Phylogenetics, Computational biology and Mitochondrial DNA in the investigation of issues within Genome. Many of his research projects under Algorithm are closely connected to Event with Event, tying the diverse disciplines of science together. As part of one scientific family, he deals mainly with the area of Metaheuristic, narrowing it down to issues related to the Multi-swarm optimization, and often Swarm behaviour.

Between 2012 and 2021, his most popular works were:

  • MITOS: Improved de novo metazoan mitochondrial genome annotation (2019 citations)
  • A comprehensive analysis of bilaterian mitochondrial genomes and phylogeny. (137 citations)
  • Phylogenomics with paralogs (80 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Algorithm
  • Gene

Martin Middendorf mainly focuses on Genome, Gene, Phylogenetic tree, Phylogenetics and Computational biology. His Genome study combines topics from a wide range of disciplines, such as Mitochondrial DNA and Bioinformatics. In his research, Sister group and Gene rearrangement is intimately related to RefSeq, which falls under the overarching field of Mitochondrial DNA.

His Phylogenetic tree research is multidisciplinary, incorporating perspectives in Tree, Construct, Dynamic programming and Heuristic. The study incorporates disciplines such as Evolutionary biology and Optimization problem in addition to Phylogenetics. Martin Middendorf combines subjects such as Annotation, Complement and Tree with his study of Computational biology.

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.

Best Publications

MITOS: Improved de novo metazoan mitochondrial genome annotation

Matthias Bernt;Alexander Donath;Frank Jühling;Frank Jühling;Fabian Externbrink.
Molecular Phylogenetics and Evolution (2013)

2214 Citations

Ant colony optimization for resource-constrained project scheduling

D. Merkle;M. Middendorf;H. Schmeck.
IEEE Transactions on Evolutionary Computation (2002)

1083 Citations

A hierarchical particle swarm optimizer and its adaptive variant

S. Janson;M. Middendorf.
systems man and cybernetics (2005)

454 Citations

Bi-Criterion Optimization with Multi Colony Ant Algorithms

Steffen Iredi;Daniel Merkle;Martin Middendorf.
international conference on evolutionary multi criterion optimization (2001)

330 Citations

Multi Colony Ant Algorithms

Martin Middendorf;Frank Reischle;Hartmut Schmeck.
Journal of Heuristics (2002)

247 Citations

A Population Based Approach for ACO

Michael Guntsch;Martin Middendorf.
Lecture Notes in Computer Science (2002)

241 Citations

Applying Population Based ACO to Dynamic Optimization Problems

Michael Guntsch;Martin Middendorf.
Lecture Notes in Computer Science (2002)

236 Citations

Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP

Michael Guntsch;Martin Middendorf.
evoworkshops on applications of evolutionary computing (2001)

232 Citations

CREx: inferring genomic rearrangements based on common intervals.

Matthias Bernt;Daniel Merkle;Kai Ramsch;Guido Fritzsch.
Bioinformatics (2007)

191 Citations

An Ant Algorithm with a New Pheromone Evaluation Rule for Total Tardiness Problems

Daniel Merkle;Martin Middendorf.
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight (2000)

183 Citations

Best Scientists Citing Martin Middendorf

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Peter F. Stadler

Leipzig University

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Marco Dorigo

Marco Dorigo

Université Libre de Bruxelles

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De Montfort University

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Thomas Stützle

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Christian Blum

Christian Blum

Spanish National Research Council

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Alfried P. Vogler

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Imperial College London

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Jun Zhang

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Chinese Academy of Sciences

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Madeleine Beekman

Madeleine Beekman

University of Sydney

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Mohammad Reza Meybodi

Mohammad Reza Meybodi

Amirkabir University of Technology

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Mauro Birattari

Mauro Birattari

Université Libre de Bruxelles

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Swagatam Das

Swagatam Das

Indian Statistical Institute

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Kenneth M. Halanych

Kenneth M. Halanych

Auburn University

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Enrique Alba

Enrique Alba

University of Malaga

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Rafael Zardoya

Rafael Zardoya

Spanish National Research Council

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Manfred Weidmann

Manfred Weidmann

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

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