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D-Index & Metrics

Computer Science

D-Index
39
Citations
8836
World Ranking
9595
National Ranking
173

Overview

Mike Preuss is affiliated with Leiden University in the Netherlands and has a broad research portfolio primarily in computer science. Their work encompasses significant contributions to artificial intelligence and optimization algorithms, with a focus on both theoretical and applied aspects.

Preuss's main fields of study include:

  • Computer Science

Their subfields cover a wide range, highlighting the interdisciplinary nature of their research:

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Sociology and Political Science
  • Computer Vision and Pattern Recognition
  • Economics and Econometrics

Preuss has contributed to research topics such as:

  • Reinforcement Learning in Robotics
  • Artificial Intelligence in Games
  • Advanced Multi-Objective Optimization Algorithms
  • Metaheuristic Optimization Algorithms Research
  • Sports Analytics and Performance
  • Evolutionary Algorithms and Applications
  • Misinformation and Its Impacts

The scientist has published extensively, with recent papers including:

  • Hybridizing Niching, Particle Swarm Optimization, and Evolution Strategy for Multimodal Optimization (2020) in IEEE Transactions on Cybernetics
  • Prediction of Player Churn and Disengagement Based on User Activity Data of a Freemium Online Strategy Game (2020) in IEEE Transactions on Games
  • High-accuracy model-based reinforcement learning, a survey (2023) in Artificial Intelligence Review
  • Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization (2021) in Computers & Operations Research
  • Models Matter: the impact of single-step retrosynthesis on synthesis planning (2024) in Digital Discovery

Their frequent co-authors include:

  • Aske Plaat
  • Alan Kai Hassen
  • Carola Doerr
  • Michael Emmerich
  • Christian Grimme

Mike Preuss has appeared multiple times in leading publication venues, notably:

  • arXiv (Cornell University)
  • KI - Künstliche Intelligenz
  • IEEE Transactions on Games
  • 2021 IEEE Conference on Games (CoG)
  • Evolutionary Computation

The researcher also has a record of book publications, mainly with Springer Science+Business Media, including:

  • Parallel Problem Solving from Nature - PPSN XVI (2020)
  • Metaheuristics for Finding Multiple Solutions (2021)
  • Disinformation in Open Online Media (2020)

Best Publications

  • Planning chemical syntheses with deep neural networks and symbolic AI

    Marwin H. S. Segler;Mike Preuss;Mark P. Waller

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

    Santiago Ontanon;Gabriel Synnaeve;Alberto Uriarte;Florian Richoux

  • Sequential parameter optimization

    T. Bartz-Beielstein;C.W.G. Lasarczyk;M. Preuss

  • Exploratory landscape analysis

    Olaf Mersmann;Bernd Bischl;Heike Trautmann;Mike Preuss

  • Experimental Methods for the Analysis of Optimization Algorithms

    Thomas Bartz-Beielstein;Marco Chiarandini;Lus Paquete;Mike Preuss

  • A Framework for Distributed Evolutionary Algorithms

    Maribel García Arenas;Pierre Collet;A. E. Eiben;Márk Jelasity

  • Algorithm selection based on exploratory landscape analysis and cost-sensitive learning

    Bernd Bischl;Olaf Mersmann;Heike Trautmann;Mike Preuß

  • Social Bots: Human-Like by Means of Human Control?

    Christian Grimme;Mike Preuss;Lena Adam;Heike Trautmann

  • Multiobjective exploration of the StarCraft map space

    Julian Togelius;Mike Preuss;Nicola Beume;Simon Wessing

  • Multimodal Optimization by Means of a Topological Species Conservation Algorithm

    C Stoean;M Preuss;R Stoean;D Dumitrescu

  • Procedural Content Generation: Goals, Challenges and Actionable Steps

    Julian Togelius;Alex J. Champandard;Pier Luca Lanzi;Michael Mateas

  • Capabilities of EMOA to detect and preserve equivalent pareto subsets

    Günter Rudolph;Boris Naujoks;Mike Preuss

  • Towards multiobjective procedural map generation

    Julian Togelius;Mike Preuss;Georgios N. Yannakakis

  • Multimodal Optimization by Means of Evolutionary Algorithms

    Mike Preuss

  • The 2009 Simulated Car Racing Championship

    Daniele Loiacono;Pier Luca Lanzi;Julian Togelius;Enrique Onieva

  • Benchmarking evolutionary algorithms: towards exploratory landscape analysis

    Olaf Mersmann;Mike Preuss;Heike Trautmann

  • Problem Definitions for Performance Assessment of Multi-objective Optimization Algorithms

    VL Huang;AK Qin;K Deb;E Zitzler

  • Pareto set and EMOA behavior for simple multimodal multiobjective functions

    Mike Preuss;Boris Naujoks;Günter Rudolph

  • Niching the CMA-ES via nearest-better clustering

    Mike Preuss

  • Experimental Research in Evolutionary Computation

    Thomas Bartz-Beielstein;Mike Preuss

  • Enhancing Decision Space Diversity in Evolutionary Multiobjective Algorithms

    Ofer M. Shir;Mike Preuss;Boris Naujoks;Michael Emmerich

Frequent Co-Authors

Günter Rudolph
Günter Rudolph TU Dortmund University
Heike Trautmann
Heike Trautmann University of Münster
Julian Togelius
Julian Togelius New York University
Georgios N. Yannakakis
Georgios N. Yannakakis University of Malta
Michael Emmerich
Michael Emmerich Leiden University
Ferrante Neri
Ferrante Neri University of Nottingham
Sebastian Risi
Sebastian Risi IT University of Copenhagen
Gianni A. Di Caro
Gianni A. Di Caro Carnegie Mellon University
Bernd Bischl
Bernd Bischl Ludwig-Maximilians-Universität München
Thomas Bäck
Thomas Bäck Leiden University

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