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.
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.
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.
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.
Planning chemical syntheses with deep neural networks and symbolic AI
Marwin H. S. Segler;Mike Preuss;Mark P. Waller.
Nature (2018)
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)
Sequential parameter optimization
T. Bartz-Beielstein;C.W.G. Lasarczyk;M. Preuss.
congress on evolutionary computation (2005)
Exploratory landscape analysis
Olaf Mersmann;Bernd Bischl;Heike Trautmann;Mike Preuss.
genetic and evolutionary computation conference (2011)
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)
Multiobjective exploration of the StarCraft map space
Julian Togelius;Mike Preuss;Nicola Beume;Simon Wessing.
computational intelligence and games (2010)
Multimodal Optimization by Means of a Topological Species Conservation Algorithm
C Stoean;M Preuss;R Stoean;D Dumitrescu.
IEEE Transactions on Evolutionary Computation (2010)
Towards multiobjective procedural map generation
Julian Togelius;Mike Preuss;Georgios N. Yannakakis.
foundations of digital games (2010)
Procedural Content Generation: Goals, Challenges and Actionable Steps
Julian Togelius;Alex J. Champandard;Pier Luca Lanzi;Michael Mateas.
computational intelligence and games (2013)
Capabilities of EMOA to detect and preserve equivalent pareto subsets
Günter Rudolph;Boris Naujoks;Mike Preuss.
international conference on evolutionary multi criterion optimization (2007)
New York University
University of Malta
University of Nottingham
Ludwig-Maximilians-Universität München
Carnegie Mellon University
University of York
Leiden University
University of California, Santa Cruz
Michigan State University
University of Sheffield
French Institute for Research in Computer Science and Automation - INRIA
Publications: 17
Profile was last updated on December 6th, 2021.
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The ranking d-index is inferred from publications deemed to belong to the considered discipline.
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