Olaf Mersmann

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Heike TrautmannProfessor of Machine Learning and Optimisation, Paderborn University, GermanyVerified email at uni-paderborn.de
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Thomas Bartz-BeielsteinTH Koeln, Institute for Data Science, Engineering, and AnalyticsVerified email at th-koeln.de
Claus WeihsProfessor für Computergestützte Statistik, TU DortmundVerified email at tu-dortmund.de
Tea TušarSenior Research Associate, Jožef Stefan InstituteVerified email at ijs.si
Nikolaus HansenInria & Ecole Polytechnique & Institut Polytechnique de Paris, FranceVerified email at Inria.fr
Anne AugerInria, CMAP Ecole Polytechnique, IP ParisVerified email at inria.fr
Mike PreussUniversiteit LeidenVerified email at liacs.leidenuniv.nl
Martin ZaeffererDHBW RavensburgVerified email at dhbw-ravensburg.de
Boris NaujoksCologne University of Applied SciencesVerified email at fh-koeln.de
Dimo BrockhoffInria, Ecole Polytechnique, Institut Polytechnique de ParisVerified email at inria.fr
Frank NeumannOptimisation and Logistics, School of ComputerVerified email at adelaide.edu.au
Markus WagnerAssociate Professor of Computer Science, Monash University, AustraliaVerified email at monash.edu
Günter RudolphProfessor in Computer Science, TU Dortmund UniversityVerified email at tu-dortmund.de
Jakob BossekPaderborn UniversityVerified email at uni-paderborn.de
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
Björn BornkampNovartis Pharma AGVerified email at novartis.com
Jörg StorkSenior Data Scientist, Otto Fuchs KGVerified email at otto-fuchs.com
Frederik RehbachCologne University of Applied SciencesVerified email at th-koeln.de
Jörg RahnenführerTU Dortmund UniversityVerified email at statistik.tu-dortmund.de

Olaf Mersmann

TH Köln

Verified email at p-value.net - Homepage

Statistics Optimization Machine Learning


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COCO: A platform for comparing continuous optimizers in a black-box setting N Hansen, A Auger, R Ros, O Mersmann, T Tušar, D Brockhoff Optimization Methods and Software 36 (1), 114-144, 2021	567	2021
Exploratory landscape analysis O Mersmann, B Bischl, H Trautmann, M Preuss, C Weihs, G Rudolph Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011	433	2011
Resampling methods for meta-model validation with recommendations for evolutionary computation B Bischl, O Mersmann, H Trautmann, C Weihs Evolutionary computation 20 (2), 249-275, 2012	240	2012
tuneR: Analysis of music and speech U Ligges, S Krey, O Mersmann, S Schnackenberg See https://CRAN. R-project. org/package= tuneR, 2018	191	2018
Evolutionary algorithms T Bartz‐Beielstein, J Branke, J Mehnen, O Mersmann Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4 (3 …, 2014	186	2014
Algorithm selection based on exploratory landscape analysis and cost-sensitive learning B Bischl, O Mersmann, H Trautmann, M Preuß Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012	179	2012
microbenchmark: Accurate timing functions O Mersmann, C Beleites, R Hurling, A Friedman R package version 1 (4-2), 1, 2015	159	2015
Benchmarking evolutionary algorithms: Towards exploratory landscape analysis O Mersmann, M Preuss, H Trautmann Parallel Problem Solving from Nature, PPSN XI: 11th International Conference …, 2010	153	2010
tuneR: Analysis of music U Ligges, S Krey, O Mersmann, S Schnackenberg URL: http://r-forge. r-project. org/projects/tuner, 2013	142	2013
A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem O Mersmann, B Bischl, H Trautmann, M Wagner, J Bossek, F Neumann Annals of Mathematics and Artificial Intelligence 69, 151-182, 2013	102	2013
Package ‘truncnorm’ O Mersmann, H Trautmann, D Steuer, B Bornkamp, MO Mersmann R package version, 1.0-8, 2018	73*	2018
BatchJobs and BatchExperiments: Abstraction mechanisms for using R in batch environments B Bischl, M Lang, O Mersmann, J Rahnenführer, C Weihs Journal of Statistical Software 64, 1-25, 2015	65	2015
Hyperparameter tuning for machine and deep learning with R: A practical guide E Bartz, T Bartz-Beielstein, M Zaefferer, O Mersmann Springer Nature, 2023	61	2023
Analyzing the BBOB results by means of benchmarking concepts O Mersmann, M Preuss, H Trautmann, B Bischl, C Weihs Evolutionary computation 23 (1), 161-185, 2015	61	2015
microbenchmark: Accurate timing functions, 2015 O Mersmann, C Beleites, R Hurling, A Friedman, JM Ulrich URL https://CRAN. R-project. org/package= microbenchmark. R package version …, 2004	61	2004
Truncnorm: Truncated normal distribution O Mersmann, H Trautmann, D Steuer, B Bornkamp R package version, 1.0-8, 2018	60	2018
Local search and the traveling salesman problem: A feature-based characterization of problem hardness O Mersmann, B Bischl, J Bossek, H Trautmann, M Wagner, F Neumann Learning and Intelligent Optimization: 6th International Conference, LION 6 …, 2012	55	2012
COCO: The experimental procedure N Hansen, T Tusar, O Mersmann, A Auger, D Brockhoff arXiv preprint arXiv:1603.08776, 2016	53	2016
mco: Multiple criteria optimization algorithms and related functions O Mersmann, H Trautmann, D Steuer, B Bischl, K Deb R package version, 1.0-15, 2014	44	2014
emoa: Evolutionary multiobjective optimization algorithms O Mersmann R package version 0.5-0, 2012	35*	2012

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