Mike Preuss
Associate professor
- Name
- Dr. M. Preuss
- Telephone
- +31 71 527 2727
- m.preuss@liacs.leidenuniv.nl
- ORCID iD
- 0000-0003-4681-1346
Mike Preuss is associate professor at LIACS, the Computer Science department of Leiden University. He is a member of the interdisciplinary research programme Society, Artificial Intelligence and Life Sciences (SAILS). He works in AI, namely game AI, natural computing, and social media computing.
More information about Mike Preuss
PhD Candidates
News
Former PhD candidates
Mike received his PhD in 2013 from the Chair of Algorithm Engineering at TU Dortmund, Germany, and was with ERCIS at the WWU Muenster, Germany, from 2013 to 2018. His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multi-modal and multi-objective optimization, and on computational intelligence and machine learning methods for computer games. He is also involved in Social Media Computing, and he was publications chair of the multi-disciplinary MISDOOM conference 2019. He is associate editor of the IEEE ToG journal and has been member of the organizational team of several conferences in the last years, in various functions, as general co-chair, proceedings chair, competition chair, workshops chair.
Associate professor
- Science
- Leiden Inst of Advanced Computer Science
- Wang H., Emmerich M.T.M., Preuss M. & Plaat A. (2023), Analysis of hyper-parameters for AlphaZero-like deep reinforcement learning, International Journal of Information Technology & Decision Making 22(2): 829-853.
- Gómez-MaureiraA.: Kniestedt I. Barbero B. & Humain Preuss M. (2022), An explorer’s journal for machines: exploring the case of Cyberpunk 2077, Journal of Gaming & Virtual Worlds 14(1): 111-135.
- Yang Z., Preuss M. & Plaat A. (2022), Transfer learning and curriculum learning in Sokoban. In: Leiva L.A., PUrski C., Markovich R., Najjar A. & Schommer C. (Eds.) Artificial Intelligence and Machine Learning. BNAIC/Benelearn 2021.. no. 1530 Cham: Springer. 187-200.
- Müller-Brockhausen M.F.T., Preuss M. & Plaat A. (2021), Procedural content generation: better benchmarks for transfer reinforcement learning. In: 2021 IEEE Conference on games (CoG). Copenhagen: IEEE.
- Wang H., Preuss M. & Plaat A. (2021), Adaptive warm-start MCTS in AlphaZero-like deep reinforcement learning. In: Pham D.N., Theeramunkong T., Governatori G. & Liu F. (Eds.) PRICAI 2021: Trends in artificial intelligence. no. LNCS-13033 Cham: Springer. 60-71.
- Müller-Brockhausen M.F.T., Preuss M. & Plaat A. (2021), A new challenge: approaching Tetris Link with AI, 2021 IEEE Conference on games (CoG). IEEE Conference on Games 17 August 2021 - 20 August 2021. Copenhagen: IEEE.
- Luo W., Lin X., Zhang J. & Preuss M. (2021), A survey of nearest-better clustering in swarm and evolutionary computation, 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE Congress on Evolutionary Computation, CEC 2021 28 June 2021 - 1 July 2021: IEEE. 1961-1967.
- Rothmeier K., Pflanzl N., Hullmann J.A. & Preuss M. (2021), Prediction of player churn and disengagement based on user activity data of a freemium online strategy game, IEEE Transactions on Games 13(1): 78-88.
- Grimme C., Kerschke P., Aspar P., Trautmann H., Preuss M., Deutz A.H., Wang H. & Emmerich M.T.M. (2021), Peeking beyond peaks: challenges and research potentials of continuous multimodal multi-objective optimization, Computers & Operations Research 136: 105489.
- Wang H., Preuss M., Emmerich M.T.M. & Plaat A. (2021), Tackling Morpion Solitaire with AlphaZero-like Ranked Reward Reinforcement Learning, 2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). 2nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) 1 September 2020 - 4 September 2020: IEEE. 149-152.
- Mozgovoy M., Preuss M. & Bidarra R. (2021), Team sports for game AI benchmarking revisited, International Journal of Computer Games Technology 2021: 1-9 (5521877 ).
- Wang H., Emmerich M.T.M., Preuss M. & Plaat A. (2020), Alternative loss functions in AlphaZero-like self-play, 2019 IEEE Symposium Series on Computational Intelligence (SSCI). 2019 IEEE Symposium Series on Computational Intelligence (SSCI) 6 December 2019 - 9 December 2019: IEEE. 155-162.
- Hagg A., Preuss M., Asteroth A. & Bäck T.H.W. (2020), An analysis of phenotypic diversity in multi-solution optimization. Filipič B., Minisci E. & Vasile M. (Eds.), International Conference on Bioinspired Methods and Their Applications. 9th International Conference, BIOMA 2020 19 November 2020 - 20 November 2020. Cham: Springer. 43-55.
- Duijn M. van, Preuss M., Spaiser V., Takes F.W. & Verberne S. (Eds.) (2020), Disinformation in Open Online Media. Lecture Notes in Computer Science no. 12259. Cham: Springer.
- Gómez Maureira M.A., Barbero G., Freese M. & Preuss M. (2020), Towards a taxonomy of AI in hybrid board games. Yannakakis G.N., Liapis A., Kyburz P., Volz V., Khosmood F. & Lopes P. (Eds.), FDG '20: Proceedings of the 15th international conference on the foundations of digital games. FDG '20: International Conference on the Foundations of Digital Games 15 September 2020 - 18 September 2020. New York, U.S.A.: Association for Computing Machinery.
- Wang H., Preuss M. & Plaat A. (2020), Warm-start AlphaZero self-play search enhancements. Bäck T.W., Preuss M., Deutz A., Wang H., Doerr C., Emmerich M.T.M. & Trautmann H. (Eds.), Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. PPSN 2020 (16th International Conference on Parallel Problem Solving from Nature) 5 September 2020 - 9 September 2020 no. Lecture Notes in Computer Science, vol. 12270. Cham: Springer. 528-542.
- Wang H., Bäck T.H.W., Plaat A., Emmerich M.T.M. & Preuss M. (2019), On the potential of evolution strategies for neural network weight optimization. Lopez-Ibanez M., Auger A. & Stützle T. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO 2019 13 July 2019 - 17 July 2019: ACM. 191-192.
- Liapis A., Yannakakis G.N., Nelson M.J., Preuss M. & Bidarra R. (2019), Orchestrating Game Generation, IEEE Transactions on Games 11(1): 48-68.
- Kerschke P., Wang H., Preuss M., Grimme G., Deutz A.H., Trautmann H. & Emmerich M.T.M. (2019), Search Dynamics on Multimodal Multi-Objective Problems, Evolutionary Computation 27(4): 577-609.
- Pappa G.L., Emmerich M.T.M., Bazzan A., Browne W., Deb K., Doerr C., Ðurasević M., Epitropakis M.G., Haraldsson S.O., Jakobovic D., Kerschke P., Krawiec K., Lehre P.K., Li X., Lissovoi A., Malo P., Martí L., Mei Y., Merelo J.J., Miller J.F., Moraglio A., Nebro A.J., Nguyen S., Ochoa G., Oliveto P., Picek S., Pillay N., Preuss M., Schoenauer M., Senkerik R., Sinha A., Shir O., Sudholt D., Whitley D., Wineberg M., Woodward J. & Zhang M. (2018), Tutorials at PPSN 2018. Auger A., Fonseca C.M., Lourenço N., Machado P., Paquete L. & Whitley D. (Eds.), International Conference on Parallel Problem Solving from Nature. PPSN: International Conference on Parallel Solving from Nature 8 September 2018 - 12 September 2018 no. Lecture Notes in Computer Science, volume 11102. Cham: Springer. 477-489.
- Segler M.H.S., Preuss M. & Waller M.P. (2018), Planning chemical syntheses with deep neural networks and symbolic AI, Nature 555: 604-610.
- Schieb C. & Preuss M. (2018), Considering the Elaboration Likelihood Model for simulating hate and counter speech on Facebook, Studies in Communication and Media 7(4): 580-606.
- Grimme C., Preuss M., Adam L. & Trautmann H. (2017), Social Bots: Human-Like by Means of Human Control?, Big Data 5(4): 279-293.
- Ahrari A., Deb K. & Preuss M. (2017), Multimodal Optimization by Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations, Evolutionary Computation 25(3): 439-471.
- Buro M., Ontanon S. & Preuss M. (2016), Guest Editorial Real-Time Strategy Games, IEEE Transactions on Computational Intelligence and AI in Games 8(4): 317-318.
- Wessing S. & Preuss M. (2016), On multiobjective selection for multimodal optimization, Computational Optimization and Applications 63(3): 875-902.
- Mersmann O., Preuss M., Trautmann H., Bischl .B. & Weihs C. (2015), Analyzing the BBOB Results by Means of Benchmarking Concepts, Evolutionary Computation 23(1): 161-185.
- Quadflieg J., Preuss M. & Rudolph G. (2014), Driving as a human: a track learning based adaptable architecture for a car racing controller, Genetic Programming and Evolvable Machines 15(4): 433-476.
- Ontanon S., Synnaeve G., Uriarte A., Richoux F., Churchill D. & Preuss M. (2013), A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft, IEEE Transactions on Computational Intelligence and AI in Games 5(4): 293-311.
- Togelius J., Preuss M., Beume N., Wessing S., Hagelback J., Yannakakis G.N. & Grappiolo C. (2013), Controllable procedural map generation via multiobjective evolution, Genetic Programming and Evolvable Machines 14(2): 245-277.
- Vatolkin I., Preuss M., Rudolph G., Eichhoff M. & Weihs C. (2012), Multi-objective evolutionary feature selection for instrument recognition in polyphonic audio mixtures, Soft Computing 16(12): 2027-2047.
- Ochoa G., Preuss M., Bartz-Beielstein T. & Schoenauer M. (2012), Editorial for the Special Issue on Automated Design and Assessment of Heuristic Search Methods, Evolutionary Computation 20(2): 161-163.
- Loiacono D., Lanzi P.L., Togelius J., Onieva E., Pelta D.A., Butz M.V., Lonneker T.D., Cardamone L., Perez D., Saez Y., Preuss M. & Quadflieg J. (2010), The 2009 Simulated Car Racing Championship, IEEE Transactions on Computational Intelligence and AI in Games 2(2): 131-147.
- Preuss M., Beume N., Danielsiek H., Hein T., Naujoks B., Piatkowski N., Stuer R., Thom A. & Wessing S. (2010), Towards Intelligent Team Composition and Maneuvering in Real-Time Strategy Games, IEEE Transactions on Computational Intelligence and AI in Games 2(2): 82-98.
- Stoean C., Preuss M., Stoean R. & Dumitrescu D. (2010), Multimodal Optimization by Means of a Topological Species Conservation Algorithm, IEEE Transactions on Evolutionary Computation 14(6): 842-864.
- Stoean R., Preuss M., Stoean C., El-Darzi E. & Dumitrescu D. (2009), Support vector machine learning with an evolutionary engine, Journal of the Operational Research Society 60(8): 1116-1122.
- Trautmann H., Wagner T., Naujoks B., Preuss M. & Mehnen J. (2009), Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms, Evolutionary Computation 17(4): 493-509.
- Stoean C., Preuss M. & Stoean R. (2009), Species Separation by a Clustering Mean towards Multimodal Function Optimization, Annals of the University of Craiova. Mathematics and Computer Science Series 36(2): 53-62.
- Henrich F., Bouvy C., Kausch C., Lucas K., Preuss M., Rudolph G. & Roosen P. (2008), Economic optimization of non-sharp separation sequences by means of evolutionary algorithms, Computers and Chemical Engineering 32(7): 1411-1432.
- Stoean R., Stoean C., Preuss M. & Dumitrescu D. (2006), Evolutionary multi-class support vector machines for classification, International Journal of Computers Communications and Control 1(5): 423-428.
- Bartz-Beielstein T., Preuss M. & Rudolph G. (2006), Investigation of one-go evolution strategy/quasi-Newton hybridizations. Almeida F. (Ed.), Hybrid Metaheuristics. HM 2006. Hybrid Metaheuristics 2006 13 October 2006 - 14 October 2006 no. LNCS 4030. Berlin, Heidelberg: Springer. 178-191.
- Giacobini M., Preuss M. & Tomassini M. (2006), Effects of scale-free and small-world topologies on binary coded self-adaptive CEA. Gottlieb J. & Raidl G.R. (Eds.), Evolutionary Computation in Combinatorial Optimization. EvoCOP 2006. European Conference on Evolutionary Computation in Combinatorial Optimization 10 April 2006 - 12 April 2019 no. LNCS 3906. Berlin Heidelberg: Springer. 86-98.
- Stoean C., Stoean R., Preuss M. & Dumitrescu D. (2006), A cooperative evolutionary algorithm for classification, International Journal of Computers Communications and Control 1(5): 417-422.
- Preuss M., Naujoks B. & Rudolph G. (2006), Pareto set and EMOA behavior for simple multimodal multiobjective functions. Runarsson T.P., Beyer H.G., Burke E., Merelo-Guervós J.J., Whitley L.D. & Yao X. (Eds.), Parallel Problem Solving from Nature - PPSN IX. Problem Solving from Nature 9 September 2006 - 13 September 2006 no. LNCS 4193. Berlin, Heidelberg: Springer. 513-522.
- Preuss M. & Lasarczyk C. (2004), On the importance of information speed in structured populations. Yao X. (Ed.), Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Parallel Problem Solving from Nature 18 September 2004 - 22 February 2019 no. 3242. Berlin, Heidelberg: Springer. 91-100.
- Jelasity M. & Preuss M. (2002), On obtaining global information in a peer-to-peer fully distributed environment. Monien B. & Feldmann R. (Eds.), Euro-Par 2002 Parallel Processing. Euro-Par 2002. Congress on Evolutionary Computation. CEC'02 12 May 2002 - 17 February 2019 no. 2400. Berlin, Heidelberg: Springer. 573-577.