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 31 Citations 5,050 150 World Ranking 8111 National Ranking 137

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Algorithm

His primary areas of investigation include Artificial intelligence, Evolutionary algorithm, Mathematical optimization, Machine learning and Multi-objective optimization. His Artificial intelligence study frequently draws connections between adjacent fields such as Heuristics. The study incorporates disciplines such as Monte Carlo tree search, Symbolic artificial intelligence, Filter and Deep neural networks in addition to Heuristics.

The various areas that he examines in his Evolutionary algorithm study include Function and Benchmark. His Machine learning research integrates issues from Data mining and Heuristic. The concepts of his Multi-objective optimization study are interwoven with issues in Pareto principle, Test functions for optimization and Search algorithm.

His most cited work include:

  • Planning chemical syntheses with deep neural networks and symbolic AI. (508 citations)
  • A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft (273 citations)
  • Sequential parameter optimization (236 citations)

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

The scientist’s investigation covers issues in Artificial intelligence, Evolutionary algorithm, Mathematical optimization, Machine learning and Evolutionary computation. Mike Preuss undertakes interdisciplinary study in the fields of Artificial intelligence and Landscape analysis through his research. Mike Preuss works mostly in the field of Evolutionary algorithm, limiting it down to topics relating to Cluster analysis and, in certain cases, Identification and Global optimization, as a part of the same area of interest.

His study in Function extends to Mathematical optimization with its themes. In his study, Data mining is strongly linked to Benchmark, which falls under the umbrella field of Machine learning. His Computational intelligence study combines topics in areas such as Game mechanics and Real-time strategy.

He most often published in these fields:

  • Artificial intelligence (46.64%)
  • Evolutionary algorithm (31.84%)
  • Mathematical optimization (26.01%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (46.64%)
  • Reinforcement learning (5.38%)
  • Machine learning (24.22%)

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

His main research concerns Artificial intelligence, Reinforcement learning, Machine learning, Mathematical optimization and Artificial neural network. His studies deal with areas such as Monte Carlo tree search and Computation as well as Artificial intelligence. His Reinforcement learning research is multidisciplinary, relying on both Taxonomy and Search algorithm.

His research integrates issues of High dimensional and Robotics in his study of Machine learning. His work in the fields of Mathematical optimization, such as Multi-objective optimization and Optimization problem, overlaps with other areas such as Focus and Ellipsoid. His Artificial neural network study incorporates themes from Evolutionary algorithm, Baseline and Feed forward.

Between 2018 and 2021, his most popular works were:

  • Orchestrating Game Generation (23 citations)
  • Search Dynamics on Multimodal Multiobjective Problems (11 citations)
  • From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI (10 citations)

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

  • Artificial intelligence
  • Machine learning
  • Algorithm

Mike Preuss focuses on Mathematical optimization, Artificial intelligence, Focus, Machine learning and Human–computer interaction. His work on Multi-objective optimization and Local search as part of general Mathematical optimization research is frequently linked to Selection, bridging the gap between disciplines. Mike Preuss combines subjects such as Local optimum, Optimization problem and Global optimum with his study of Multi-objective optimization.

His Artificial intelligence study focuses on Artificial neural network in particular. His research in Machine learning intersects with topics in Computation and Minification. Mike Preuss has included themes like Simple, Recommender system, Feature and The Internet in his Human–computer interaction study.

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

Planning chemical syntheses with deep neural networks and symbolic AI

Marwin H. S. Segler;Mike Preuss;Mark P. Waller.
Nature (2018)

851 Citations

A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft

Santiago Ontanon;Gabriel Synnaeve;Alberto Uriarte;Florian Richoux.
IEEE Transactions on Computational Intelligence and AI in Games (2013)

486 Citations

Sequential parameter optimization

T. Bartz-Beielstein;C.W.G. Lasarczyk;M. Preuss.
congress on evolutionary computation (2005)

378 Citations

Exploratory landscape analysis

Olaf Mersmann;Bernd Bischl;Heike Trautmann;Mike Preuss.
genetic and evolutionary computation conference (2011)

240 Citations

Experimental Methods for the Analysis of Optimization Algorithms

Thomas Bartz-Beielstein;Marco Chiarandini;Lus Paquete;Mike Preuss.
Experimental Methods for the Analysis of Optimization Algorithms 1st (2010)

163 Citations

Multiobjective exploration of the StarCraft map space

Julian Togelius;Mike Preuss;Nicola Beume;Simon Wessing.
computational intelligence and games (2010)

155 Citations

Multimodal Optimization by Means of a Topological Species Conservation Algorithm

C Stoean;M Preuss;R Stoean;D Dumitrescu.
IEEE Transactions on Evolutionary Computation (2010)

139 Citations

Towards multiobjective procedural map generation

Julian Togelius;Mike Preuss;Georgios N. Yannakakis.
foundations of digital games (2010)

131 Citations

Procedural Content Generation: Goals, Challenges and Actionable Steps

Julian Togelius;Alex J. Champandard;Pier Luca Lanzi;Michael Mateas.
computational intelligence and games (2013)

125 Citations

Capabilities of EMOA to detect and preserve equivalent pareto subsets

Günter Rudolph;Boris Naujoks;Mike Preuss.
international conference on evolutionary multi criterion optimization (2007)

119 Citations

Best Scientists Citing Mike Preuss

Julian Togelius

Julian Togelius

New York University

Publications: 74

Georgios N. Yannakakis

Georgios N. Yannakakis

University of Malta

Publications: 52

Thomas Bäck

Thomas Bäck

Leiden University

Publications: 33

Thomas Stützle

Thomas Stützle

Université Libre de Bruxelles

Publications: 31

Hisao Ishibuchi

Hisao Ishibuchi

Southern University of Science and Technology

Publications: 26

Simon M. Lucas

Simon M. Lucas

Queen Mary University of London

Publications: 24

A. E. Eiben

A. E. Eiben

Vrije Universiteit Amsterdam

Publications: 23

Bartosz A. Grzybowski

Bartosz A. Grzybowski

Ulsan National Institute of Science and Technology

Publications: 23

Boyang Qu

Boyang Qu

Zhongyuan University of Technology

Publications: 22

Ponnuthurai Nagaratnam Suganthan

Ponnuthurai Nagaratnam Suganthan

Nanyang Technological University

Publications: 21

Frank Neumann

Frank Neumann

University of Adelaide

Publications: 21

Carlos A. Coello Coello

Carlos A. Coello Coello

CINVESTAV

Publications: 20

Holger H. Hoos

Holger H. Hoos

Leiden University

Publications: 20

Klavs F. Jensen

Klavs F. Jensen

MIT

Publications: 19

Marc Schoenauer

Marc Schoenauer

French Institute for Research in Computer Science and Automation - INRIA

Publications: 17

Jun Zhang

Jun Zhang

Chinese Academy of Sciences

Publications: 15

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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