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Computational Intelligence
Fakultät für Informatik
Bild mit Personen des Lehrstuhlpersonals © Lehrstuhl für Algorithmic Engineering Fakultät für Informatik der TU-Dortmund
Arbeitsgruppe Computational Intelligence

Prof. Dr. Günter Rudolph

Curriculum Vitae

11/1996 Promotion in Informatik (Dr. rer. nat.), Universität Dortmund

04/1991 Hauptdiplom in Informatik (Diplom-Informatiker), Universität Dortmund

04/1987 Vordiplom in Informatik (cand. inform.), Universität Karlsruhe (TH)

06/1983 Allgemeine Hochschulreife (Abitur), Ruhr-Gymnasium Witten

04/2005 - heute Universitäts-Professor, Fakultät für Informatik, TU Dortmund

06/2001 - 03/2005 Produkt- und Softwareentwickler, Parsytec AG, Aachen

01/1997 - 05/2001 Wissenschaftlicher Mitarbeiter, Sonderforschungsbereich 531, Universität Dortmund

02/1994 - 12/1996 Wissenschaftlicher Mitarbeiter, Informatik Centrum Dortmund (ICD)

05/1991 - 01/1994 Wissenschaftlicher Mitarbeiter, Fachbereich Informatik, Universität Dortmund

Lehre

WS 2022/23 (Vorschau)

Introduction to Computational Intelligence (040309) 2V + 1Ü

Seminar 2S

Seminar über Bachelor-, Master- und Doktorarbeiten (049115) 2S

 

SS 2022

Praktische Optimierung (041221) 4V + 2Ü

Seminar über Bachelor-, Master- und Doktorarbeiten (049115) 2S

 

WS 2021/22

Introduction to Computational Intelligence (040309) 2V + 1Ü

Ausgewählte Kapitel der Computational Intelligence (042501) 2V

Übungen zu Ausgewählte Kapitel der Computational Intelligence (042502) 2Ü

Seminar über Bachelor-, Master- und Doktorarbeiten (049115) 2S

 

SS 2021

Praktische Optimierung (041221) 4V + 2Ü

Proseminar: Umweltinformatik (040605) 2PS

Seminar über Bachelor-, Master- und Doktorarbeiten (049115) 2S

 

WS 2020/21

Introduction to Computational Intelligence (040309) 2V + 1Ü

Einführung in die Programmierung (048001) 4V + 2Ü (+ 4P)

Musikdatenanalyse (040341) 2V + 1Ü

Fachprojekt: Musikinformatik (040275) 4P

Seminar über Bachelor-, Master- und Doktorarbeiten (049115) 2S

 

SS 2020

Praktische Optimierung (041221) 4V + 2Ü

Seminar über Bachelor-, Master- und Doktorarbeiten (049115) 2S

 

WS 2019/20

Introduction to Computational Intelligence (040309) 2V + 1Ü

Einführung in die Programmierung (048001) 4V + 2Ü (+ 4P)

Fachprojekt: Musikinformatik(TBD) 4P

Seminar über Bachelor-, Master- und Doktorarbeiten (049115) 2S

 

SS 2019

Forschungssemester

 

WS 2018/19

Introduction to Computational Intelligence (040309) 2V + 1Ü

Einführung in die Programmierung (048001) 4V + 2Ü (+ 4P)

Seminar über Bachelor-, Master-, Diplom- und Doktorarbeiten (049115) 2S

 

SS 2018

Praktische Optimierung (041221) 4V + 2Ü

Mathematik für Informatiker II (040503) 4V + 2Ü

Musikdatenanalyse (040341) 2V + 1Ü

Fachprojekt: Digital Entertainment Technologies (040268) 4P

Seminar über Bachelor-, Master-, Diplom- und Doktorarbeiten (049115) 2S

 

WS 2017/18

Introduction to Computational Intelligence (040309) 2V + 1Ü

Einführung in die Programmierung (048001) 4V + 2Ü (+ 4P)

Seminar: Entertainment Computing (041407) 2S

Seminar über Bachelor-, Master-, Diplom- und Doktorarbeiten (049115) 2S

 

SS 2017

Praktische Optimierung (041221) 4V + 2Ü

Mathematik für Informatiker II (040503) 4V + 2Ü

Fachprojekt: Digital Entertainment Technologies (040268) 4P

Seminar über Bachelor-, Master-, Diplom- und Doktorarbeiten (049115) 2S

 

WS 2016/17

Introduction to Computational Intelligence (040309) 2V + 1Ü

Einführung in die Programmierung (048001) 4V + 2Ü (+ 4P)

Seminar über Bachelor-, Master-, Diplom- und Doktorarbeiten (049115) 2S

 

SS 2016

Musikdatenanalyse (04xxxx) 2V + 1Ü Blockvorlesung im September!

Praktische Optimierung (041221) 4V + 2Ü

Mathematik für Informatiker II (040503 )4V + 2Ü

Fachprojekt: Digital Entertainment Technologies (040268) 4P

Proseminar: Basistechnologien der Spieleprogrammierung (040605) 2PS

Seminar über Bachelor-, Master-, Diplom- und Doktorarbeiten (049115) 2S

 

WS 2015/16

Introduction to Computational Intelligence (040309) 2V + 1Ü

Einführung in die Programmierung (048001) 4V + 2Ü (+ 4P)

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

SS 2015

Forschungssemester

 

WS 2014/15

Introduction to Computational Intelligence (040309) 2V + 1Ü

Einführung in die Programmierung (048001) 4V + 2Ü (+ 4P)

Seminar über Diplom- und Doktorarbeiten(049115) 2S

 

SS 2014

Praktische Optimierung (041221) 4V + 2Ü

Mathematik für Informatiker II (040503) 4V + 2Ü

Fachprojekt: Digital Entertainment Technologies (040268) 4P

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

WS 2013/14

Introduction to Computational Intelligence (040309)2V + 1Ü

Einführung in die Programmierung (048001) 4V + 2Ü (+ 4P)

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

SS 2013

Praktische Optimierung (041221) 4V + 2Ü

Mathematik für Informatiker II (040503) 4V + 2Ü

Fachprojekt: Digital Entertainment Technologies (040268) 4P

Proseminar: Fuzzy Systeme in der Anwendung (040605) 2PS (+1)

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

WS 2012/13

Introduction to Computational Intelligence (040309) 2V + 1Ü

Einführung in die Programmierung (048001) 4V + 2Ü (+ 4P)

Proseminar: Bionische Optimierung (040605) 2PS (+1)

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

SS 2012

Praktische Optimierung (041221) 4V + 2Ü

Mathematik für Informatiker II (040503) 4V + 2Ü

Fachprojekt: Digital Entertainment Technologies (040268) 4P

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

WS 2011/12

Introduction to Computational Intelligence (040309) 2V + 1Ü

Einführung in die Programmierung (048001) 4V + 2Ü (+ 4P)

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

SS 2011

Praktische Optimierung (041221) 4V + 2Ü

Ausgewählte Kapitel der Computational Intelligence (042501) 2V

Übungen zu Ausgewählte Kapitel der Computational Intelligence (042502) 2Ü

Proseminar: Computerlinguistik (040605) 2PS (+1)

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

WS 2010/11

Introduction to Computational Intelligence (040309) 2V + 1Ü

Einführung in die Programmierung (048001) 4V + 2Ü (+ 4P)

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

SS 2010

Praktische Optimierung (041221) 4V + 2Ü Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

WS 2009/10
Introduction to Computational Intelligence (040309) 2V + 1Ü

Einführung in die Programmierung (048001) 4V + 2Ü (+ 4P)

Proseminar: Kreative Algorithmen (040605) 2PS (+1)

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

SS 2009

Forschungsfreisemester gemäß §40 Abs. 1 HG

 

WS 2008/09

Praktische Optimierung (041221 )4V + 2Ü

Einführung in die Programmierung (EINI) (048001)4V + 2Ü + 4P

Seminar: Aktuelle Forschungsgebiete der Musikdatenanalyse (041413) 2S

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

SS 2008

Ausgewählte Kapitel der Computational Intelligence (042501) 2V

Übungen zu Ausgewählte Kapitel der Computational Intelligence (042502) 1Ü

Seminar: Computational Intelligence und Musikinformatik (041405) 2S

Proseminar: Medieninformatik (040605) 2PS

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

WS 2007/08

Praktische Optimierung (042417) 4V + 2Ü

Einführung in die Programmierung (EINI) (048001) 4V + 2Ü + 4P

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

SS 2007

Data Mining mit CI-Methoden (042325) 2V

Übung zu Data Mining mit CI-Methoden (042326) 1Ü

Seminar: Computational Intelligence bei Computerspielen (044625) 2S

Proseminar: Musikinformatik (040716) 2PS

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

WS 2006/07

Fundamente der Computational Intelligence (042277) 4V + 2Ü

Einführung in die Programmierung (EINI) (048001) 4V + 2Ü + 4P

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

SS 2006

Multikriterielle Optimierung mit Metaheuristiken (042217) 2V

Seminar: Planung und Analyse von Computer-Experimenten (044586) 2S

Proseminar: Multithreading-Techniken (040705) 2PS

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

WS 2005/06

Fundamente der Computational Intelligence (042179) 4V + 2Ü

Einführung in die Programmierung (EINI) (048001) 4V + 2Ü + 4P

Seminar über Diplom- und Doktorarbeiten (049115) 2S

 

SS 2005

Multikriterielle Optimierung mit Metaheuristiken (042817) 2V

Seminar: Evolutionäre Algorithmen in der Bioinformatik (044555) 2S

Seminar über Diplom- und Doktorarbeiten (049115) 2S

Aktuelle Arbeitsgebiete

Unter dem Terminus Computational Intelligence (CI) verstehen wir das Studium der Informationsverarbeitung in natürlichen, insbesondere biologischen, Systemen und die Umsetzung der dabei gewonnenen Erkenntnisse in algorithmische Konzepte für Problemstellungen, die sich mit herkömlichen Methoden auf digitalen Rechnern nur schwer oder noch gar nicht bearbeiten lassen.

Ursprünglich wurden der CI nur die algorithmischen Konzepte der künstlichen neuronalen Netze (NN), der evolutionären Algorithmen (EA) und der Fuzzy-Systeme (FS) zugerechnet. Diese wurden mittlerweile etwa durch die algorithmischen Konzepte der Schwarmintelligenz (SI) und der künstlichen Immunsysteme (IS) ergänzt.

Bei Fuzzy-Systemen bildet man die menschliche Fähigkeit nach, auch mit unscharfen Begriffen und Angaben erfolgreich Informationen verarbeiten zu können und etwa Schlußfolgerungen daraus zu ziehen. Typische Anwendungsgebiete sind unscharfe Regler und zunehmend die Wissensentdeckung (Data Mining).

Künstliche Neuronale Netze sind die algorithmische Umsetzung biologisch inspirierter Modelle der Informationsverarbeitung im Gehirn und Nervensystem. Anstatt einen Lösungsweg explizit ausprogrammieren und analysieren zu müssen, nutzt man etwa die Fähigkeit neuronaler Netze, eine Lösungsstrategie anhand von präsentierten Beispielen zu erlernen. Ein typisches Anwendungsgebiet ist die Mustererkennung (z.B. zur Anomaliedetektion, Signalklassifikation oder Spracherkennung).

Der Prozess der Variation und Auslese im Rahmen der genetischen Vererbung wird bei den evolutionären Algorithmen als ein iterativer Verbesserungsprozess aufgefasst und entsprechend umgesetzt. Typische Anwendungsgebiete sind Optimierungsprobleme, für die keine Spezialverfahren der mathematischen Optimierung zu Verfügung stehen.

Beim Konzept der Schwarm-Intelligenz wird das erwünschte Gesamtverhalten eines Systems durch das vernetzte Einzelverhalten zahlreicher Individuen ohne eine zentrale Steuerung hervorgerufen. So führt die Modellierung des Sozialverhaltens von Ameisenkolonien zu den sogenannten Ameisenalgorithmen für die kombinatorische Optimierung, während Partikelschwarmverfahren den Bewegungen von Vogel- oder Fischschwärmen nachempfunden sind und zur kontinuierlichen Optimierung eingesetzt werden.

Die künstlichen Immunnetzwerke sind inspiriert vom Immunsystem der Wirbeltiere, welches zwischen körpereigenen und körperfremden Zellen unterscheiden und dieses Wissen im Immunsystem dynamisch speichern kann. Typisches Anwendungsgebiet ist die Mustererkennung und hier speziell die Klassifikation.

Unsere Arbeitsgebiete im Bereich CI lassen sich grob in drei Schwerpunkte gliedern:

Theorie:
Zum Verständnis von CI-Methoden ist eine detaillierte formale Analyse ihrer Arbeitsweise notwendig. Dies beinhaltet auch eine theoretische Betrachtung der Leistungsfähigkeit dieser Methoden auf verschiedenen Einsatzgebieten.

Operationalisierung:
Das Ausmaß des Erfolgs bei Anwendungen von CI-Methoden hängt wesentlich von dem Erfahrungswissen und der Intuition des beteiligten CI-Experten ab. Um auch weniger CI-erfahrenen Anwendern zu einem Erfolg zu verhelfen, entwickeln wir formale Richtlinien und Anleitungen dafür, wie die theoretischen algorithmischen CI-Konzepte zuverlässig in eine konkete Anwendung umgesetzt werden sollten.

Anwendung:
Neben den klassischen Anwendungsentwicklungen in den Bereichen Maschinenbau, Elektrotechnik und chemischer Verfahrenstechnik sollen nun verstärkt auch biotechnologische Problemstellungen bearbeitet werden.

Weitere Informationen

Sprechstunde:

  • Dienstags 10:30 - 11:30 (während der Vorlesungszeit, sonst nach Vereinbarung)

Publications

  1. G. Rudolph: Convergence Properties of Evolutionary Algorithms, Hamburg: Kovac 1997, ISBN 3-86064-554-4.
  1. M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J.J. Merelo, and H.-P. Schwefel (eds.): Proceedings of the 6th International Conference on Parallel Problem Solving from Nature - PPSN VI, Berlin and Heidelberg: Springer 2000.
  2. W. B. Langdon, E. Cantu-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schulz, J. F. Miller, E. Burke, and N. Jonoska (eds.): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), San Francisco (CA): Morgan Kaufmann Publishers 2002.
  3. T. Bartz-Beielstein, G. Jankord, B. Naujoks, G. Rudolph, and K. Schmitt (eds.): Festschrift Hans-Paul Schwefel 2006, Universität Dortmund, Dortmund 2006 (ISBN 3-921823-34-X).
  4. T. Bartz-Beielstein, M.J. Blesa Aguilera, C. Blum, B. Naujoks, A. Roli, G. Rudolph, M. Samples (eds.): Hybrid Metaheuristics, Proceedings of the 4th International Workshop (HM 2007), Lecture Notes in Computer Science Vol. 4771, Springer: Berlin and Heidelberg 2007.
  5. G. Rudolph, T. Jansen, S. Lucas, C. Poloni, and N. Beume (eds.): Proceedings of the 10th International Conference on Parallel Problem Solving from Nature - PPSN X, Lecture Notes in Computer Science Vol. 5199, Springer: Berlin and Heidelberg 2008.
  6. R. Schaefer, C. Cotta, J. Kolodziej, and G. Rudolph (eds.): Proceedings of the 11th International Conference on Parallel Problem Solving from Nature - PPSN XI, Lecture Notes in Computer Science Vol. 6238 & 6239, Springer: Berlin and Heidelberg 2010.
  7. S. Greco, K. Klamroth, J. D. Knowles, and G. Rudolph (eds.): Understanding Complexity in Multiobjective Optimization, Dagstuhl Reports, Volume 5, Issue 1, pp. 96-163, doi: 10.4230/DagRep.5.1.96, 2015.
  8. C. Weihs, D. Jannach, I. Vatolkin, and G. Rudolph (eds.): Music Data Analysis: Foundations and Applications, CRC Press, November 2016.
  9. H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, and C. Grimme (eds.): Proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2017), Lecture Notes in Computer Science Vol. 10173, Springer, 2017.
  1. G. Rudolph: Massively Parallel Simulated Annealing and its Relation to Evolutionary Algorithms, Evolutionary Computation 1(4):361-382, 1994.
  2. G. Rudolph: Convergence Properties of Canonical Genetic Algorithms, IEEE Transactions on Neural Networks 5(1):96-101, 1994.
  3. G. Rudolph and H.-P. Schwefel: Evolutionäre Algorithmen: Ein robustes Optimierkonzept, Physikalische Blätter 50(3):236-238, 1994.
  4. G. Yin, G. Rudolph, and H.-P. Schwefel: Establishing connections between evolutionary algorithms and stochastic approximation, Informatica 6(1):93-116, 1995.
  5. G. Yin, G. Rudolph, and H.-P. Schwefel: Analyzing (1,lambda) Evolution Strategy via Stochastic Approximation Methods, Evolutionary Computation 3(4):473-489, 1996.
  6. G. Rudolph: How Mutation and Selection Solve Long Path-Problems in Polynomial Expected Time, Evolutionary Computation 4(2):195-205, 1997.
  7. G. Rudolph: Convergence Rates of Evolutionary Algorithms for a Class of Convex Objective Functions, Control and Cybernetics 26(3):375-390, 1997.
  8. G. Rudolph: Local Convergence Rates of Simple Evolutionary Algorithms with Cauchy Mutations, IEEE Transactions on Evolutionary Computation 1(4):249-258, 1997.
  9. G. Rudolph: Finite Markov Chain Results in Evolutionary Computation: A Tour d'Horizon, Fundamenta Informaticae 35(1-4):67-89, 1998.
  10. A. E. Eiben and G. Rudolph: Theory of Evolutionary Algorithms: A Bird's Eye View, Theoretical Computer Science 229(1):3-9, 1999.
  11. G. Rudolph: Self-Adaptive Mutations May Lead to Premature Convergence, IEEE Transactions on Evolutionary Computation 5(4):410-414, 2001.
  12. K. Weinert, J. Mehnen, and G. Rudolph: Dynamic Neighborhood Structures in Parallel Evolution Strategies, Complex Systems 13(3):227-243, 2001.
  13. G. Rudolph: Analysis of a Non-Generational Mutationless Evolutionary Algorithm for Separable Fitness Functions, International Journal of Computational Intelligence Research 1(1):77-84, 2005.
  14. R. Klinger and G. Rudolph: Automatic Composition of Music with Methods of Computational Intelligence, WSEAS Transactions on Information Science & Applications 4(3):508-517, 2007.
  15. F. Henrich, C. Bouvy, Ch. Kausch, K. Lucas, M. Preuß, G. Rudolph, and P. Roosen: Economic optimization of non-sharp separation sequences by means of evolutionary algorithms, Computers and Chemical Engineering 32(7):1411-1432, 2008
  16. N. Beume, B. Naujoks, and G. Rudolph: SMS-EMOA: Effektive evolutionäre Mehrzieloptimierung, Automatisierungstechnik (at) 56(7):357-364, 2008.
  17. Zhiyong Li, G. Rudolph, and Kenli Li: Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms, Computers and Mathematics with Applications 57(11/12):1843-1854, 2009.
  18. H. Blume, B. Bischl, M. Botteck, C. Igel, R. Martin, G. Rötter, G. Rudolph, W. Theimer, I. Vatolkin, and C. Weihs: Huge Music Archives on Mobile Devices, IEEE Signal Processing Magazine 28(4):24-39, 2011.
  19. I. Vatolkin, M. Preuß, G. Rudolph, M. Eichhoff, and C. Weihs: Multi-Objective Evolutionary Feature Selection for Instrument Recognition in Polyphonic Audio Mixtures, Soft Computing 16(12):2027-2047, 2012. Online: DOI 10.1007/s00500-012-0874-9 .
  20. G. Rudolph, H. Trautmann, and O. Schütze: Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen. Automatisierungstechnik (at) 60(10):612-621, 2012.
  21. A. Agapie, M. Agapie, G. Rudolph, and G. Zbaganu: Convergence of Evolutionary Algorithms on the n-dimensional Continuous Space. IEEE Transactions on Cybernetics 43(5):1462-1472, 2013.
  22. J. Quadflieg, M. Preuss, and G. Rudolph: Driving as a human: a track learning based adaptable architecture for a car racing controller. Genetic Programming and Evolvable Machines 15(2):433-476, 2014. (DOI 10.1007/s10710-014-9227-z)
  23. S. Wessing, G. Rudolph, S. Turck, C. Klimmek, S. C. Schäfer, M.Schneider, and U. Lehmann: Replacing FEA for sheet metal forming by surrogate modeling, Cogent Engineering 1:950853, 2014. (DOI dx.doi.org/10.1080/23311916.2014.950853)
  24. Shaomiao Chen, Zhiyong Li, Bo Yang, and G. Rudolph: Quantum-inspired Hyper-heuristics for Energy-aware Scheduling on Heterogeneous Computing Systems. IEEE Transactions on Parallel & Distributed Systems 27(6):1796-1810, 2016 (doi: 10.1109/TPDS.2015.2462835).
  25. G. Rudolph, O. Schütze, C. Grimme, C. Dominguez-Medina, and H. Trautmann: Optimal Averaged Hausdorff Archives for Bi-objective Problems: Theoretical and Numerical Results. Computational Optimization and Applications 64(2):589-618, 2016 (doi: 10.1007/s10589-015-9815-8).
  26. O. Schütze, V. A. Sosa Hernández, H. Trautmann, and G. Rudolph: The Hypervolume based Directed Search Method for Multi-Objective Optimization Problems. Journal of Heuristics 22(3), 273-300, 2016 (doi: 10.1007/s10732-016-9310-0).
  27. G. Rudolph and S. Wessing: Linear Time Estimators for Assessing Uniformity of Point Samples in Hypercubes. Informatica 27(2):335-349, 2016 (doi: 10.15388/Informatica.2016.88).
  28. K. Klamroth, S. Mostaghim, B. Naujoks, S. Poles, R. Purshouse, G. Rudolph, S. Ruzika, S. Sayìn, M. M. Wiecek, and X. Yao: Multiobjective Optimization for Interwoven Systems, Journal of Multi-Criteria Decision Analysis 24(1-2):71-81, 2017 (doi: 10.1002/mcda.1598).
  29. C. Jung, M. Zaefferer, T. Bartz-Beielstein, and G. Rudolph: Meta-model based Optimization of Hot Rolling Processes in the Metal Industry, International Journal of Advanced Manufacturing Technology 90(1-4):421-435, 2017 (doi: 10.1007/s00170-016-9386-6).
  30. M. Zaefferer, T. Bartz-Beielstein, and G. Rudolph: An Empirical Approach For Probing the Definiteness of Kernels, Soft Computing 23(21):10939-10952, 2019. First Online: 26 November 2018.
  31. L. Uribe, J. M. Bogoya, A. Vargas, A. Lara, G. Rudolph, and O. Schütze: A Set Based Newton Method for the Averaged Hausdorff Distance for Multi-objective Reference Set Problems, Mathematics 8(10):1822, 2020 (ISSN 2227-7390).
  32. F. Ostermann, I. Vatolkin, and G. Rudolph: Evaluating Creativity in Automatic Reactive Accompaniment of Jazz Improvisation, Transactions of the International Society for Music Information Retrieval 4(1):210–222, 2021. DOI: doi.org/10.5334/tismir.90
  33. F. Rehbach, M. Zaefferer, A. Fischbach, G. Rudolph, Th. Bartz-Beielstein: Benchmark-Driven Configuration of a Parallel Model-Based Optimization Algorithm, IEEE Transactions on Evolutionary Computation, accepted for publication.
  1. G. Rudolph: Evolution Strategies, pp. B1.3.1-6, in T. Bäck, D.B. Fogel, and Z. Michalewicz (eds.): Handbook of Evolutionary Computation, IOP Publishing and Oxford University Press: Bristol and New York 1997.
    Also published on pp. 81-88 in T. Bäck et al. (eds): Evolutionary Computation 1 - Basic Algorithms and Operators, IOP Publishing; Bristol 2000.
  2. G. Rudolph: Stochastic Processes, pp. B2.2.1-8, in T. Bäck, D.B. Fogel, and Z. Michalewicz (eds.): Handbook of Evolutionary Computation, IOP Publishing and Oxford University Press: Bristol and New York 1997.
  3. G. Rudolph: Modes of Stochastic Convergence, pp. B2.3.1-3, in T. Bäck, D.B. Fogel, and Z. Michalewicz (eds.): Handbook of Evolutionary Computation, IOP Publishing and Oxford University Press: Bristol and New York 1997.
  4. G. Rudolph: Local Performance Measures: Genetic Algorithms, pp. B2.4.20-27, in T. Bäck, D.B. Fogel, and Z. Michalewicz (eds.): Handbook of Evolutionary Computation, IOP Publishing and Oxford University Press: Bristol and New York 1997.
  5. G. Rudolph and J. Ziegenhirt: Computation time of evolutionary operators, pp. E2.2.1-4, in T. Bäck, D.B. Fogel, and Z. Michalewicz (eds.): Handbook of Evolutionary Computation, IOP Publishing and Oxford University Press: Bristol and New York 1997.
    Also published on pp. 247-252 in T. Bäck et al. (eds): Evolutionary Computation 2 - Advanced Algorithms and Operators, IOP Publishing; Bristol 2000.
  6. S. Droste, Th. Jansen, G. Rudolph H.-P. Schwefel, K. Tinnefeld, and I.Wegener: Theory of evolutionary algorithms and genetic programming, pp. 107-144 in H.-P. Schwefel, I. Wegener und K. Weinert (eds.): Advances in Computational Intelligence. Springer, Berlin und Heidelberg 2003.
  7. G. Rudolph: Parallel Evolution Strategies, pp. 155-169 in E. Alba (ed.): Parallel Metaheuristics: A New Class of Algorithms. Wiley: Hoboken (NJ) 2005.
  8. G. Rudolph: A Time Travel to the Early Theory of Evolution Strategies, pp. 85-89 in T. Bartz-Beielstein et al. (eds.): Festschrift Hans-Paul Schwefel 2006, University of Dortmund, Dortmund 2006 (ISBN 3-921823-34-X).
  9. G. Rudolph and H.-P. Schwefel: Simulated Evolution under Multiple Criteria Revisited, pp. 248-260 in J.M. Zurada et al. (eds.): WCCI 2008 Plenary/Invited Lectures, Springer: Berlin 2008.
  10. E.-G. Talbi, S. Monastghim, T. Okabe, H. Ishibuchi, G. Rudolph, and C.A. Coello Coello: Parallel Approaches for Multiobjective Optimization, pp. 349-372 in J. Branke et al. (eds): Multiobjective Optimization - Interactive and Evolutionary Approaches, Springer: Berlin 2008.
  11. G. Rudolph: Evolutionary Strategies, pp. 673-698 in G. Rozenberg, T. Bäck, and J.N. Kok (eds.): Handbook of Natural Computing. Springer, 2013.
  12. G. Rudolph: Stochastic Convergence, pp. 847-869 in G. Rozenberg, T. Bäck, and J.N. Kok (eds.): Handbook of Natural Computing. Springer, 2013.
  13. I. Vatolkin, B. Bischl, G. Rudolph, and C. Weihs: Statistical Comparison of Classifiers for Multi-objective Feature Selection in Instrument Recognition, pp. 171-178 in M. Spiliopoulou, L. Schmidt-Thieme and R. Janning (eds.): Data Analysis, Machine Learning and Knowledge Discovery, Springer, 2014.
  14. M. Preuss, S. Wessing, G. Rudolph, and G. Sadowski: Solving Phase Equilibrium Problems by Means of Avoidance-based Multiobjectivization, pp. 1159-1171 in J. Kacprzyk and W. Pedrycz (eds.): Springer Handbook of Computational Intelligence. Springer, 2015.
  15. S. Wessing, G. Rudolph, and M. Preuss: Assessing Basin Identification Methods for Locating Multiple Optima, pp. 53-69 in P. M. Pardalos, A. Zhigljavsky, and J. Žilinskas (eds.): Advances in Stochastic and Deterministic Global Optimization, Springer, 2016.
  16. V. A. Sosa-Hernandez, A. Lara, H. Trautmann, G. Rudolph, and O. Schütze: The Directed Search Method for Unconstrained Parameter Dependent Multi-Objective Optimization Problems, pp. 281-330 in O. Schütze et al. (eds.): Numerical and Evolutionary Optimization - NEO 15, Results of the Numerical and Evolutionary Optimization Workshop NEO 2015, Springer International, 2017.
  17. G. Rudolph: Digital Representation of Music, pp. 177-196 in C. Weihs et al. (eds.): Music Data Analysis: Foundations and Applications, CRC Press, 2017.
  18. G. Rudolph: Optimization, pp. 263-282 in C. Weihs et al. (eds.): Music Data Analysis: Foundations and Applications, CRC Press, 2017.
  1. G. Rudolph: Global Optimization by Means of Distributed Evolution Strategies, pp. 209-213 in H.-P. Schwefel and R. Männer (eds.): Parallel Problem Solving from Nature, Berlin: Springer 1991.
  2. G. Rudolph: Parallel Approaches to Stochastic Global Optimization, pp. 256-267 in W. Joosen and E. Milgrom (eds.): Parallel Computing: From Theory to Sound Practice, Proceedings of the European Workshop on Parallel Computing (EWPC 92), Amsterdam: IOS Press 1992.
  3. G. Rudolph: On Correlated Mutations in Evolution Strategies, pp. 105-114 in: R. Männer and B. Manderick: Parallel Problem Solving from Nature, 2. Amsterdam: Elsevier 1992.
  4. G. Rudolph: Parallel Clustering on a Unidirectional Ring, pp. 487-493 in R. Grebe et al. (eds.): Transputer Applications and Systems '93; Vol. 1, Amsterdam: IOS Press 1993.
  5. Th. Bäck, G. Rudolph, and H.-P. Schwefel: Evolutionary Programming and Evolution Strategies: Similarities and Differences, pp. 11-22 in D.B. Fogel and W. Atmar (eds.): Proceedings of the 2nd Annual Conference on Evolutionary Programming, La Jolla, CA: Evolutionary Programming Society 1993.
  6. G. Rudolph: Convergence of Non-Elitist Strategies, pp. 63-66 in: Proceedings of the First IEEE Conference on Evolutionary Computation, Vol. 1, Piscataway, NJ: IEEE Press 1994.
  7. G. Rudolph: An Evolutionary Algorithm for Integer Programming, pp. 139-148 in Y. Davidor, H.-P. Schwefel, and R. Männer (eds.): Parallel Problem Solving From Nature, 3. Berlin: Springer 1994.
  8. H.-P. Schwefel and G. Rudolph: Contemporary Evolution Strategies, pp. 893-907 in F. Morana et al. (eds.): Advances in Artificial Life. Berlin: Springer 1995. (revised version)
  9. G. Rudolph and J. Sprave: A cellular genetic algorithm with self-adjusting acceptance threshold, pp. 365-372 in Proceedings of the First IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, London: IEE Press 1995.
  10. G. Rudolph and J. Sprave: Significance of Locality and Selection Pressure in the Grand Deluge Evolutionary Algorithm, pp. 686-695 in H.-M. Voigt et al. (eds.): Parallel Problem Solving From Nature - PPSN IV. Berlin: Springer 1996.
  11. G. Rudolph: On interactive evolutionary algorithms and stochastic Mealy automata, pp. 218-226 in H.-M. Voigt et al. (eds.): Parallel Problem Solving From Nature - PPSN IV. Berlin: Springer 1996.
  12. G. Rudolph: Convergence of Evolutionary Algorithms in General Search Spaces, pp. 50-54 in: Proceedings of the Third IEEE Conference on Evolutionary Computation, Piscataway, NJ: IEEE Press 1996.
    Best paper award.
  13. M. Höhfeld and G. Rudolph: Towards a Theory of Population-Based Incremental Learning, pp. 1-5 in: Proceedings of the 4th IEEE Conference on Evolutionary Computation, Piscataway, NJ: IEEE Press 1997.
  14. G. Rudolph: Reflections on Bandit Problems and Selection Methods in Uncertain Environments, pp. 166-173 in T. Bäck (ed.): Proceedings of the 7th International Conference on Genetic Algorithms (ICGA '97), San Francisco, CA: Morgan Kaufmann 1997.
  15. G. Rudolph: Asymptotical Convergence Rates of Simple Evolutionary Algorithms under Factorizing Mutation Distributions, pp. 223-233 in: J.K. Hao, E. Lutton, E. Ronald, M. Schoenauer, and D. Snyers (eds.): Proceedings of Artificial Evolution '97, Berlin: Springer 1998.
  16. G. Rudolph: Evolutionary Search for Minimal Elements in Partially Ordered Finite Sets, pp. 345-353 in V.W. Porto, N. Saravanan, D. Waagen, and A.E. Eiben (eds.): Evolutionary Programming VII, Proceedings of the 7th Annual Conference on Evolutionary Programming, Berlin: Springer 1998.
  17. G. Rudolph: On a Multi-Objective Evolutionary Algorithm and Its Convergence to the Pareto Set, pp. 511-516 in: Proceedings of the 5th IEEE Conference on Evolutionary Computation, IEEE Press, Piscataway (NJ) 1998.
  18. G. Rudolph: On Risky Methods for Local Selection under Noise, pp. 169-177 in Th. Bäck, A.E. Eiben, M. Schoenauer, and H.-P. Schwefel (eds.): Parallel Problem Solving From Nature - PPSN V, Berlin: Springer 1998.
  19. M. Laumanns, G. Rudolph, and H.-P. Schwefel: A Spatial Predator-Prey Approach to Multi-Objective Optimization: A Preliminary Study, pp. 241-249 in Th. Bäck, A.E. Eiben, M. Schoenauer, and H.-P. Schwefel (eds.): Parallel Problem Solving From Nature - PPSN V, Berlin: Springer 1998.
  20. G. Rudolph: Self-Adaptation and Global Convergence: A Counter-Example, pp. 646-651 in: Proceedings of the Congress on Evolutionary Computation (CEC'99), Vol. 1, IEEE Press, Piscataway (NJ) 1999. (revised version)
  21. G. Rudolph: On Takeover Times in Spatially Structured Populations: Array and Ring, pp. 144-151 in K. K. Lai, O. Katai, M. Gen, and B. Lin (eds.): Proceedings of the 2nd Asia-Pacific Conference on Genetic Algorithms and Applications, Hong Kong: Global-Link Publishing Company 2000.
  22. G. Rudolph: Takeover Times and Probabilities of Non-Generational Selection Rules, pp. 903-910 in D. Whitley et al. (eds.): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), San Francisco (CA): Morgan Kaufmann 2000.
  23. G. Rudolph and A. Agapie: Convergence Properties of Some Multi-Objective Evolutionary Algorithms, pp. 1010-1016 in A. Zalzala et al. (eds.): Proceedings of the 2000 Congress on Evolutionary Computation (CEC 2000), Vol. 2, IEEE Press, Piscataway (NJ) 2000.
  24. G. Rudolph: Some Theoretical Properties of Evolutionary Algorithms under Partially Ordered Fitness Values, pp. 9-22 in Cs. Fabian and I. Intorsureanu (eds.): Proceedings of the Evolutionary Algorithms Workshop (EAW-2001), Bucharest, Romania, January 2001.
  25. G. Rudolph: Evolutionary Search under Partially Ordered Fitness Sets, pp. 818-822 in M.F. Sebaaly (ed.): Proceedings of the International NAISO Congress on Information Science Innovations (ISI 2001), ICSC Academic Press: Millet/Sliedrecht 2001. (ISBN 3-906454-25-8)
  26. G. Rudolph: Takeover Times of Noisy Non-Generational Selection Rules that Undo Extinction, pp. 268-271 in V. Kurkova et al. (eds.): Proceedings of the 5th International Conference on Artificial Neural Nets and Genetic Algorithms (ICANNGA 2001), Springer, Vienna 2001.
  27. G. Rudolph: A Partial Order Approach to Noisy Fitness Functions, pp. 318-325 in: J.-H. Kim, B.-T. Zhang, G. Fogel, and I. Kuscu (eds.): Proceedings of the 2001 IEEE Congress on Evolutionary Computation (CEC 2001), IEEE Press, Piscataway (NJ) 2001.
  28. M. Laumanns, G. Rudolph, and H.-P. Schwefel: Mutation Control and Convergence in Evolutionary Multi-Objective Optimization, pp. 24-29 in R. Matousek and P. Osmera (eds.): Proceedings of the 7th International Conference on Soft Computing (MENDEL 2001), Brno University of Technology, Brno, Czech Republic, 2001. (ISBN 80-214-1894-X)
  29. F. Hoffmann, T. Nierobisch, T. Seyffarth, and G. Rudolph: Visual Servoing with Moments of SIFT Features, pp. 4262-4267 in: Proceedings of the 2006 IEEE Conference on Systems, Man, and Cybernetics (IEEE SMC 2006), IEEE Press: Piscataway (NJ) 2006.
  30. M. Preuss, B. Naujoks, and G. Rudolph: Pareto Set and EMOA Behavior for Simple Multimodal Multiobjective Functions, pp. 513-522 in: T.P. Runarsson et al. (eds): Proceedings of the Ninth International Conference on Parallel Problem Solving from Nature (PPSN IX), Springer, Berlin 2006.
  31. G. Rudolph: Takeover Time in Parallel Populations with Migration, pp. 63-72 in: B. Filipic and J. Silc (eds.): Proceedings of the Second International Conference on Bioinspired Optimization Methods and their Applications (BIOMA 2006), Josef Stefan Institute: Ljubljana 2006.
  32. G. Rudolph: Deployment Scenarios of Parallelized Code in Stochastic Optimization, pp. 3-11 in: B. Filipic and J. Silc (eds.): Proceedings of the Second International Conference on Bioinspired Optimization Methods and their Applications (BIOMA 2006), Josef Stefan Institute: Ljubljana 2006. (Remark: invited paper)
  33. J. Mehnen, T. Wagner, and G. Rudolph: Evolutionary Optimization of Dynamic Multi-objective Test Functions , in: Proceedings of the Second Italian Workshop on Evolutionary Computation (GSICE2), September 2006, Siena (Italy), published on CD-ROM.
  34. T. Bartz-Beielstein, M. Preuss, and G. Rudolph: Investigation of One-Go Evolution Strategy/Quasi-Newton Hybridizations, pp. 178-191 in: F. Almeida et al. (eds.): Proceedings of the Third International Workshop on Hybrid Metaheuristics (HM 2006), Springer: Berlin 2006.
  35. N. Beume and G. Rudolph: Faster S-Metric Calculation by Considering Dominated Hypervolume as Klee's Measure Problem, pp. 231-236 in: B. Kovalerchuk (ed.): Proceedings of the Second IASTED Conference on Computational Intelligence, ACTA Press: Anaheim 2006.
    Extended version: Technical Report CI-216/06, SFB 531, University of Dortmund, June 2006.
  36. R. Klinger and G. Rudolph: Evolutionary Composition of Music with Learned Melody Evaluation, pp. 234-239 in N. Mastorakis and A. Cecchi (Eds.): Proceedings of the 5th WSEAS Int. Conf. on Computational Intelligence, Man-Machine Systems and Cybernetics, 2006.
    Best student paper award (R. Klinger).
  37. N. Beume, B. Naujoks, and G. Rudolph: Mehrkriterielle Optimierung durch evolutionäre Algorithmen mit S-Metrik-Selektion, pp. 1-10 in: R. Mikut and M. Reischl (eds.): Proceedings of the 16th GMA Workshop Computational Intelligence, Universitätsverlag Karlsruhe, 2006.  
    Young researcher award (N. Beume).
  38. G. Rudolph, B. Naujoks, and M. Preuss: Capabilities of MOEA to Detect and Preserve Equivalent Pareto Subsets, pp. 36-50 in S. Obayashi et al. (eds.): Proceedings of the 4th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2007), Springer: Berlin 2007.
  39. M. Preuss, G. Rudolph, and F. Tumakaka: Solving Multimodal Problems via Multiobjective Techniques with Application to Phase Equilibrium Detection, pp. 2703-2710 in K.C. Tan et al. (eds.): Proceedings of the 2007 IEEE Congress on Evolutionary Computation, IEEE Press: Piscataway (NJ) 2007.
  40. G. Rudolph and M. Preuss: Ein mehrkriterielles Evolutionsverfahren zur Bestimmung des Phasengleichgewichts von gemischten Flüssigkeiten, pp. 177-185 in R. Mikut and M. Reischl (eds.): Proceedings of the 17th GMA Workshop on Computational Intelligence, Universitätsverlag Karlsruhe 2007. ISBN 978-3-86644-191-0  
  41. Zhiyong Li, Zhe Li, and G. Rudolph: On the Convergence Properties of Quantum-Inspired Multi-Objective Evolutionary Algorithms, pp. 245-255 in D.-S. Huang, L. Heutte, and M. Loog (eds.): Proceedings of the International Conference on Intelligent Computing (ICIC 2007), Springer: Berlin 2007.
  42. M. Sathe, G. Rudolph, and K. Deb: Design and Validation of a Hybrid Interactive Reference Point Methods for Multi-Objective Optimization, pp. 2914-2921 in Proceedings of the 2008 IEEE Congress on Evolutionary Computation, IEEE Press: Piscataway (NJ) 2008.
  43. T. Voß, N. Beume, G. Rudolph, and C. Igel: Scalarization versus Indicator-based Selection in Multi-Objective CMA Evolution Strategies. pp. 3041-3048 in Proceedings of the 2008 IEEE Congress on Evolutionary Computation, IEEE Press: Piscataway (NJ) 2008.
  44. G. Rudolph and M. Preuss: Ein Evolutionsverfahren zur Approximation äquivalenter Urbilder von Pareto-optimalen Zielvektoren, pp. 30-39 in R. Mikut and M. Reischl (eds.): Proceedings of the 18th GMA Workshop on Computational Intelligence, Universitätsverlag Karlsruhe 2008.
  45. N. Beume, B. Naujoks, M. Preuss, G. Rudolph, and T. Wagner: Effects of 1-Greedy S-Metric-Selection on Innumerably Large Pareto Fronts, pp. 21-35 in M. Ehrgott et al. (eds.): Proceedings of 5th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2009), Springer: Berlin and Heidelberg 2009.
  46. G. Rudolph and M. Preuss: A Multiobjective Approach for Finding Equivalent Inverse Images of Pareto-optimal Objective Vectors, pp. 74-79 in C. Coello Coello, P.P. Bonissone, and Y. Jin (ed.): 2009 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (IEEE MCDM 2009), IEEE Press: Piscataway (NJ) 2009.
  47. I. Vatolkin, W. Theimer, and G. Rudolph: Design and Comparison of Different Evolution Strategies for Feature Selection and Consolidation in Music Classification, pp. 174-181 in Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC 2009), IEEE Press: Piscataway (NJ) 2009.
  48. P. Koch, O. Kramer, G. Rudolph, and N. Beume: On the Hybridization of SMS-EMOA and Local Search for Multiobjective Continuous Optimization, pp. 603-610 in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2009), ACM Press: New York 2009.
  49. O. Kramer, A. Barthelmes, and G. Rudolph: Surrogate Constraint Functions for CMA Evolutions Strategies, pp. 169-178 in B. Mertsching et al. (eds.): KI 2009 - Advances in Artificial Intelligence. Proceedings of the 32nd Annual Conference on Artificial Intelligence, LNAI 5803, Springer: Berlin and Heidelberg 2009.
  50. M. Preuss, G. Rudolph, and S. Wessing: Tuning Optimization Algorithms for Real-World Problems by Means of Surrogate Modeling, pp. 401-408 in M. Pelikan and J. Branke (Eds.): Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2010), ACM Press: New York 2010.
  51. J. Quadflieg, M. Preuss, O. Kramer, and G. Rudolph: Learning the Track and Planning Ahead in a Car Racing Controller, pp. 395-402 in Proceedings of the IEEE Symposium on Computational Intelligence and Games (CIG 2010), IEEE Press: Piscataway (NJ) 2010.
  52. N. Beume, M. Laumanns, and G. Rudolph: Convergence Rates of (1+1) Evolutionary Multiobjective Algorithms, pp. 597-606 in R. Schaefer et al. (Eds.): Proceedings of the 11th Int'l Conf. on Parallel Problem Solving from Nature (PPSN XI), Springer: Berlin Heidelberg 2010.
    Best student paper award (N. Beume).
  53. S. Wessing, N. Beume, G. Rudolph, and B. Naujoks: Parameter Tuning Boosts Performance of Variation Operators in Multiobjective Optimization, pp. 728-737 in R. Schaefer et al. (Eds.): Proceedings of the 11th Int'l Conf. on Parallel Problem Solving from Nature (PPSN XI), Springer: Berlin Heidelberg 2010.
  54. N. Beume, M. Laumanns, and G. Rudolph: Convergence Rates of SMS-EMOA on Continuous Bi-Objective Problem Classes, pp.243-251 in H.-G. Beyer and W. B. Langdon (Eds.): Proceedings of the 11th Int'l Conf. on Foundations of Genetic Algorithms (FOGA XI), ACM Press: New York 2011.
  55. J. Quadflieg, M. Preuss, and G. Rudolph: Driving Faster Than a Human Player, pp. 143-152 in C. Di Chio et al. (Eds.): Proceedings of Int'l Conf. on Applications of Evolutionary Computation (EvoApplications), part 1, Springer: Berlin Heidelberg 2011.
  56. G. Rudolph: On Geometrically Fast Convergence to Optimal Dominated Hypervolume of Set-based Multiobjective Evolutionary Algorithms, pp. 1718-1722 in A. Smith (ed.): Proceedings of 2011 IEEE Congress on Evolutionary Computation (CEC 2011), IEEE Press: Piscataway (NJ) 2011.
  57. F. Neumann, P. Oliveto, G. Rudolph, and D. Sudholt: On the Effectiveness of Crossover for Migration in Parallel Evolutionary Algorithms, pp. 1587-1594 in N. Krasnogor and P.L. Lanzi (eds.): Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2011), ACM Press: New York 2011.
  58. M. Preuss, G. Rudolph, and I. Vatolkin: Multi-Objective Feature Selection in Music Genre and Style Recognition Tasks, pp. 411-418 in N. Krasnogor and P.L. Lanzi (eds.): Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2011), ACM Press: New York 2011.
  59. M. Preuss, S. Wessing, and G. Rudolph: When Parameter Tuning Actually is Parameter Control, pp. 821-828 in N. Krasnogor and P.L. Lanzi (eds.): Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2011), ACM Press: New York 2011.
  60. O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs, and G. Rudolph: Exploratory Landscape Analysis, pp. 829-836 in N. Krasnogor and P.L. Lanzi (eds.): Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2011), ACM Press: New York 2011. ACM/Sigevo Impact Award 2021
  61. T. Deinert, I. Vatolkin, and G. Rudolph: Regression-Based Tempo Recognition from Chroma and Energy Accents for Slow Audio Recordings, pp. 60-68 in K. Brandenburg and M. Sandler (eds.): Proceedings of AES 42nd Int'l Conf. on Semantic Audio, Audio Engineering Society: New York 2011.
  62. M. Preuss, J. Quadflieg, G. Rudolph: TORCS Sensor Noise Removal and Multi-objective Track Selection for Driving Style Adaptation, pp. 337-344 in: Proceedings of the IEEE 2011 Conference on Computational Intelligence and Games, IEEE Press 2011. (DOI 10.1109/CIG.2011.6032025)
  63. K. Gerstl, G. Rudolph, O. Schütze, and H. Trautmann: Finding Evenly Spaced Fronts for Multiobjective Control via Averaging Hausdorff-Measure, in: Proceedings of 8th International Conference on Electrical Engineering, Computer Science and Automatic Control (CCE 2011), IEEE Press 2011. (DOI 10.1109/ICEEE.2011.6106656)
  64. K. Gerstl, G. Rudolph, O. Schütze, and H. Trautmann, Gleichmäßige Paretofront-Approximationen für mehrkriterielle Kontrollprobleme unter Verwendung des gemittelten Hausdorff-Maßes, pp. 93-106 in F. Hoffmann and E. Hüllermeier (eds.): Proceedings of the 21st GMA Workshop on Computational Intelligence, Universitätsverlag Karlsruhe 2011. (German version of CCE 2011)
  65. V. Mattern, I. Vatolkin, and G. Rudolph: A Case Study about the Effort to Classify Music Intervals by Chroma and Spectrum Analysis, pp. 519-528 in: B. Lausen, D. van den Poel, and A. Ultsch (eds.): Algorithms from and for Nature and Life, (revised selected papers of the 35th GfKl 2011), Springer: Cham Heidelberg 2013.
  66. H. Trautmann, G. Rudolph, C. Dominguez-Medina, and O. Schütze: Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems, pp. 89-105 in O. Schütze et al. (eds.): EVOLVE - A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation II (Proceedings), Springer: Berlin Heidelberg 2013.
  67. D. Brockhoff, M. López-Ibáñez, B. Naujoks, and G. Rudolph: Runtime Analysis of Simple Interactive Evolutionary Biobjective Optimization Algorithms, pp. 123-132 in C.A. Coello Coello et al. (Eds.): Proceedings of the 12th Int'l Conf. on Parallel Problem Solving from Nature (PPSN XII), Volume 1, Springer: Berlin Heidelberg 2012.
  68. G. Rudolph, H. Trautmann, S. Sengupta, and O. Schütze: Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation, pp. 443-459 in R.C. Purshouse et al. (eds.): 7th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2013), LNCS 7811, Springer: Berlin Heidelberg 2013.
  69. G. Rudolph: Convergence Rates of Evolutionary Algorithms for Quadratic Convex Functions with Rank-Deficient Hessian, pp. 151-160 in M. Tomassini et al. (eds.): 11th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA 2013), LNCS 7824, Springer: Berlin Heidelberg 2013.
  70. S. Wessing, M. Preuss, and G. Rudolph: Niching by Multiobjectivization with Neighbor Information: Trade-offs and Benefits. pp. 103-110 in Proceedings of 2013 IEEE Congress on Evolutionary Computation (CEC 2013), IEEE Press: Piscataway (NJ) 2013.
  71. C. Dominguez-Medina, G. Rudolph, O. Schütze, and H. Trautmann: Evenly Spaced Pareto Fronts of Quad-objective Problems using PSA Partitioning Technique. pp. 3190-3197 in Proceedings of 2013 IEEE Congress on Evolutionary Computation (CEC 2013), IEEE Press: Piscataway (NJ) 2013.
  72. V. Sosa, O. Schütze, G. Rudolph, and H. Trautmann: The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume. pp. 189-205 in EVOLVE - A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation IV (Proceedings), Springer: Berlin Heidelberg 2013.
  73. M. Kuchem, M. Preuss, and G. Rudolph: Multi-Objective Assessment of Pre-Optimized Build Orders exemplified for StarCraft 2. pp. 1-8 in IEEE Conference on Computational Intelligence in Games (CIG 2013), IEEE Press: Piscataway (NJ) 2013.
  74. G. Rudolph, O. Schütze, C. Grimme, and H. Trautmann: An Aspiration Set EMOA based on Averaged Hausdorff Distances, pp. 153-156 in P.M. Pardalos, M.G.C. Resende, C. Vogiatzis, and J.L. Walteros (eds.): Proceedings of 8th Conference on Learning and Intelligent Optimization (LION 8), Springer: Berlin Heidelberg 2014.
  75. M. Preuss, P. Voll, A. Bardow, and G. Rudolph: Looking for Alternatives: Optimization of Energy Supply Systems without Superstructure. pp. 177-188 in A.I. Esparcia-Alcázar and A.M. Mora (eds.): Proceedings of the 2014 European Conference on Applications of Evolutionary Algorithms (EvoApps 2014), Springer: Berlin Heidelberg 2014.
  76. P. Kerschke, M. Preuss, C. Hernández, O. Schütze, J.-Q. Sun, C. Grimme, G. Rudolph, B. Bischl, and H. Trautmann: Cell Mapping Techniques for Exploratory Landscape Analysis, pp. 115-131 in A.-A. Tantar et al. (eds.): Proceedings of EVOLVE - A bridge between Probability, Set Oriented Numerics and Evolutionary Computation V, Springer: Berlin Heidelberg 2014.
  77. G. Rudolph, O. Schütze, C. Grimme, and H. Trautmann: A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets, pp. 261-273 in A.-A. Tantar et al. (eds.): Proceedings of EVOLVE - A bridge between Probability, Set Oriented Numerics and Evolutionary Computation V, Springer: Berlin Heidelberg 2014.
  78. T. Glasmachers, B. Naujoks, and G. Rudolph: Start Small, Grow Big? Saving Multi-objective Function Evaluations, pp. 579-588 in T. Bartz-Beielstein, J. Branke, B. Filipič, and J. Smith (eds.): Proceedings of 13th Int'l Conference on Parallel Problem Solving from Nature (PPSN XIII), Springer: Berlin Heidelberg 2014.
  79. R. Kalkreuth, G. Rudolph and J. Krone: Automatische Generierung von Bildoperationsketten mittels genetischer Programmierung und CMA-Evolutionsstrategie, pp. 95-112 in F. Hoffmann and E. Hüllermeier (eds.): Proceedings of the 24th GMA Workshop on Computational Intelligence, Universitätsverlag Karlsruhe 2014.
  80. V.A. Sosa Hernández, O. Schütze, H. Trautmann, and G. Rudolph: On the Behavior of Stochastic Local Search within Parameter Dependent MOPs, pp. 126-140 in A. Gaspar-Cunha et al. (eds.): Proceedings of 8th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2015), Part II, LNCS 9019, Springer: Cham Heidelberg 2015.
  81. I. Vatolkin, G. Rudolph, and C. Weihs: Interpretability of Music Classication as a Criterion for Evolutionary Multi-Objective Feature Selection, pp. 236-248 in C. Johnson et al. (eds.): Proceedings of 4th Int'l Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART), LNCS 9027, Springer: Cham Heidelberg 2015.
  82. C. Grimme, S. Meisel, H. Trautmann, G. Rudolph, and M. Wölck: Multi-objective Analysis of Approaches to Dynamic Routing of a Vehicle, accepted for publication in proceedings of 23rd European Conference on Information Systems (ECIS 2015), 26 - 29 May 2015, Münster (Germany).
  83. L. Marti, C. Grimme, P. Kerschke, H. Trautmann, and G. Rudolph: Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms, pp. 1427-1428 in Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO 2015), ACM Press: New York 2015. (doi 10.1145/2739482.2764631)
    Extended version available.
  84. J. Bossek, B. Bischl, T. Wagner, and G. Rudolph: Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement, pp. 1319-1326 in S. Silva (ed.): Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2015), ACM Press: New York 2015.
  85. S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, and H. Trautmann: Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle, pp. 425-432 in S. Silva (ed.): Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2015), ACM Press: New York 2015.
  86. J. Quadflieg, G. Rudolph, and M. Preuss: How Costly is a Good Compromise: Multi-Objective TORCS Controller Parameter Optimization, pp. 454-460 in IEEE Conference on Computational Intelligence in Games (CIG 2015), IEEE Press: Piscataway (NJ), 2015.
  87. K. Majchrzak, J. Quadflieg, and G. Rudolph: Advanced Dynamic Scripting for Fighting Game AI, pp. 86-99 in K. Chorianopoulos et al. (eds.): Proceedings of 14th Int'l Conference on Entertainment Computing (ICEC 2015), Springer International, Cham 2015.
  88. I. Vatolkin, G. Rudolph, and C. Weihs: Evaluation of Album Effect for Feature Selection in Music Genre Recognition , in M. Müller and F. Wiering (eds.): Proceedings of 16th Int'l Society for Music Information Retrieval Conference (ISMIR 2015). ISBN 987-84-606-8853-2.
  89. D.A. Menges, S. Wessing, and G. Rudolph: Asynchrone Parallelisierung des SMS-EMOA zur Parameteroptimierung von mobilen Robotern, pp. 47-65 in F. Hoffmann and E. Hüllermeier (eds.): Proceedings of 25th GMA Workshop on Computational Intelligence, Universitätsverlag Karlsruhe 2015.  
  90. R. Kalkreuth, G. Rudolph, and J. Krone: Improving Convergence in Cartesian Genetic Programming Using Adaptive Crossover, Mutation and Selection, pp. 1415 - 1422 in Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI 2015), IEEE Press: Piscataway (NJ) 2015.
  91. G. Rudolph, O. Schütze, and H. Trautmann: On the Closest Averaged Hausdorff Archive for a Circularly Convex Pareto Front, pp. 42-55 in: G. Squillero and P. Burelli (eds.): Applications of Evolutionary Computation, Proceedings of 19th European Conference (EvoApps 2016), Part II, LNCS 9598, Springer 2016.
  92. R. Kalkreuth, G. Rudolph, and J. Krone: More Efficient Evolution of Small Genetic Programs in Cartesian Genetic Programming by Using Genotypic Age, pp. 5052--5059 in Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016), IEEE Press 2016.
  93. V. Volz, G. Rudolph, and B. Naujoks: Demonstrating the Feasibility of Automatic Game Balancing, pp. 269-276 in: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2016), ACM Press: New York 2016. Best Paper Award (of Tracks DETA + PES + SBSE)
  94. S. Wessing, G. Rudolph, and D. Menges: Comparing Asynchronous and Synchronous Parallelization of the SMS-EMOA, pp. 558-567 in J. Handl, E. Hart, P. R. Lewis, M. López-Ibáñez, G. Ochoa, and B. Paechter (eds.): Proceedings of 14th Int'l Conf. on Parallel Problem Solving from Nature (PPSN XIV), Springer 2016.
  95. V. Volz, G. Rudolph, and B. Naujoks: Surrogate-Assisted Partial Order-based Evolutionary Optimisation, pp. 639-653 in H. Trautmann et al. (eds.): Proceedings of 9th Int'l Conf. on Evolutionary Multi-Criterion Optimization (EMO 2017), Springer 2017.
  96. S. Wessing, R. Pink, K. Brandenbusch, and G. Rudolph: Toward Step-size Adaptation in Evolutionary Multiobjective Optimization, pp. 670-684 in H. Trautmann et al. (eds.): Proceedings of 9th Int'l Conf. on Evolutionary Multi-Criterion Optimization (EMO 2017), Springer 2017.
  97. R. Kalkreuth, G. Rudolph, and A. Droschinsky: A New Subgraph Crossover for Cartesian Genetic Programming, pp. 294–310, in J. McDermott, M.Castelli, L. Sekanina, E. Haasdijk, and P. García-Sánchez, P. (eds.): Proceedings of 20th European Conference on Genetic Programming (EuroGP 2017), Springer 2017.
  98. F. Ostermann, I. Vatolkin, and G. Rudolph: Evaluation Rules for Evolutionary Generation of Drum Patterns in Jazz Solos, pp. 246-261 in J. Correia, V. Ciesielski, and A. Liapis (eds.): Proceedings of 6th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART 2017), Springer 2017.
  99. F. Scholz, I. Vatolkin, and G. Rudolph: Singing Voice Detection across Different Music Genres, Paper 2.1 in Ch. Dittmar and J. Abeßer: Proceedings of the Conference on Semantic Audio (AES 2017), Audio Engineering Society: New York 2017. (ISBN 978-1-942220-15-2)
  100. V. Volz, G. Rudolph, and B. Naujoks: Investigating Uncertainty Propagation in Surrogate-Assisted Evolutionary Algorithms, pp. 881-888 in: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2017), ACM Press: New York 2017.
  101. J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann: Local Search Effects in Bi-Objective Orienteering, pp. 585-592 in: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2018), ACM Press: New York 2018.
  102. I. Vatolkin and G. Rudolph: Comparison of Audio Features for Recognition of Western and Ethnic Instruments in Polyphonic Mixtures, pp. 554-560 in: Proceedings of 19th Int'l Society for Music Information Retrieval Conference (ISMIR 2018). Paris (France), 23-27 September 2018.
  103. M. Bommert and G. Rudolph: Reliable Biobjective Solution of Stochastic Problems Using Metamodels, pp. 581-592 in Deb, K. et al. (eds.): Proceedings of 10th Int'l Conf. on Evolutionary Multi-Criterion Optimization (EMO 2019), Springer 2019.
  104. J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann: Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm, pp. 516-528 in Deb, K. et al. (eds.): Proceedings of 10th Int'l Conf. on Evolutionary Multi-Criterion Optimization (EMO 2019), Springer 2019.
  105. J. Kuzmic, G. Rudolph, W. Roth, and M. Rübsam: IoT Based Driver Information System for Monitoring the Load Securing, pp. 262-269 in: Proceedings of 4th Int'l Conf. on Internet of Things, Big Data and Security (IoTBDS 2019), Volume 1, SciTePress 2019. (doi: 10.5220/0007710302620269) Best Poster Award
  106. Ziqing Cheng, Qi Wang, Zhiyong Li, and G. Rudolph: Computation Offloading and Resource Allocation for Mobile Edge Computing, pp. 2735-2740 in: Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI 2019), 2019. (doi: 10.1109/SSCI44817.2019.9003106)
  107. Chen Du, Yifan Chen, Zhiyong Li, and G. Rudolph: Joint Optimization of Offloading and Communication Resources in Mobile Edge Computing, pp. 2729-2734 in: Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI 2019), 2019. (doi: 10.1109/SSCI44817.2019.9003099)
  108. F. Heerde, I. Vatolkin, and G. Rudolph: Comparing Fuzzy Rule Based Approaches for Music Genre Classification, pp. 35-48 in J. Romero et al. (eds.): Proceedings of 9th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART 2020), Springer International Publishing: Cham 2020.
  109. J. Kuzmic and G. Rudolph: Unity 3D Simulator of Autonomous Motorway Traffic applied to Emergency Corridor Building, pp. 197-204, in: G. Wills, P. Kacsuk, and V. Chang (eds.): Proceedings of 5th Int'l Conf. on Internet of Things, Big Data and Security (IoTBDS 2020), 2020. (doi:10.5220/0009349601970204)
  110. J. Bossek, C. Grimme, G. Rudolph, and H. Trautmann: Towards Decision Support in Dynamic Bi-Objective Vehicle Routing, in: Proceedings of 2020 IEEE Congress on Evolutionary Computation (CEC 2020), IEEE Press, 2020. (doi: 10.1109/CEC48606.2020.9185778)
  111. P. Ginsel, I. Vatolkin, and G. Rudolph: Analysis of Structural Complexity Features for Music Genre Recognition, in: Proceedings of 2020 IEEE Congress on Evolutionary Computation (CEC 2020), IEEE Press, 2020. (doi: 10.1109/CEC48606.2020.9185540)
  112. M. Hamdan, G. Rudolph, and N. Hochstrate: A Parallel Evolutionary System for Multi-objective Optimisation, in: Proceedings of 2020 IEEE Congress on Evolutionary Computation (CEC 2020), IEEE Press, 2020. (doi: 10.1109/CEC48606.2020.9185855)
  113. M. Bommert and G. Rudolph: Reliable Solution of Multidimensional Stochastic Problems Using Metamodels, pp. 215-226 in G. Nicosia et al. (eds.): Proceedings of 6th Int'l Conf. on Machine Learning, Optimization, and Data Science (LOD 2020), Springer International: Cham 2020. (doi: 10.1007/978-3-030-64583-0_20)
  114. M. Bommert and G. Rudolph: Kernel Density Estimation for Reliable Biobjective Solution of Stochastic Problems, pp. 53-64 in H. Ishibuchi et al. (eds.): Proceedings of 11th Int'l Conf. on Evolutionary Multi-Criterion Optimization (EMO 2021), Springer 2021.
  115. J. Kuzmic and G. Rudolph: Comparison between Filtered Canny Edge Detector and Convolutional Neural Network for Real Time Lane Detection in a Unity 3D Simulator, pp. 148-155, in: Proceedings of 6th Int'l Conf. on Internet of Things, Big Data and Security (IoTBDS 2021), Volume 1, SciTePress 2021. (doi: 10.5220/0010383701480155)
  116. I. Vatolkin, P. Ginsel, and G. Rudolph: Advancements in the Music Information Retrieval Framework AMUSE over the Last Decade, pp. 2383–2389 in: Proceedings of 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021), 2021. (doi: 10.1145/3404835.3463252)
  117. J. Kuzmic and G. Rudolph: Object Detection with TensorFlow on Hardware with Limited Resources for Low-Power IoT Devices, pp. 302-309, in: Proceedings of 13th International Conference on Neural Computation Theory and Applications (NCTA 2021), 2021. (doi: 10.5220/0010653500003063)
  118. J. Kuzmic and G. Rudolph: Real-time Distance Measurement in a 2D Image on Hardware with Limited Resources for Low-power IoT Devices (Radar Control System), accepted for publication in: Proceedings of 3rd International Conference on Deep Learning Theory and Applications (DeLTA 2022), Lisbon, 12-14 July 2022.
  1. G. Rudolph: Globale Optimierung mit parallelen Evolutionsstrategien, Diplomarbeit, Fachbereich Informatik, Universität Dortmund, Germany, July 1990.
  2. M. Laumanns, G. Rudolph, and H.-P. Schwefel: Adaptive Mutation Control in Panmictic And Spatially Distributed Multi-objective Evolutionary Algorithms, PPSN/SAB Workshop on Multiobjective Problem Solving from Nature (MPSN), Paris, September 2000.
  3. Z. Li and G. Rudolph: A Framework of Quantum-inspired Multi-Objective Evolutionary Algorithms and its Convergence Condition, Accepted for poster presentation at GECCO 2007, London (UK), July 2007.
  4. B. Künne, G. Rudolph, B. Naujoks, T. Richard, B. Schultebraucks: A multiobjective evolutionary algorithm for designing and optimizing gearshafts. pp. 267-268 in P. Scharff (ed.): 53. Internationales Wissenschaftliches Kolloquium (IWK) der TU Ilmenau (Proceedings CD-Rom), ISLE, TU Ilmenau 2008 (ISBN 978-3-938843-40-6).
  5. G. Rudolph: Introduction (to an interview with Hans-Paul Schwefel by Pier Luca Lanzi), SIGEVOlution 3(4):2, Winter 2008.
  6. G. Rudolph: The Virtues of Metaheuristics in Stochastic Programming, pp. 55-57 in A. Borkowski and M. Nagl (eds.): Abstracts of the 1st Polish-German Workshop on Research Co-operation in Computer Science. Polish Academy of Science, Warsaw 2009 (ISBN 978-83-924901-7-3).
  7. G. Rudolph, M. Preuss, and J. Quadflieg: Double-layered Surrogate Modeling for Tuning Metaheuristics, presented at ENBIS/EMSE Conference "Design and Analysis of Computer Experiments", Saint-Etienne (France), July 1-3, 2009.
  8. Available as Technical Report.
  9. P. Spronck, G. Yannakakis, C. Bauckhage, E. André, D. Loiacono, and G. Rudolph: Player Modeling, pp. 59-61 in S.M. Lucas et al. (eds:): Artificial and Computational Intelligence in Games, Dagstuhl Reports 2(5):43–70, 2012.
  10. M. Preuss and G. Rudolph: Conference Report on IEEE CIG 2014. IEEE Computational Intelligence Magazine 10(1):14-15, 2015.
  11. H. Ishibuchi, K. Klamroth, S. Mostaghim, B. Naujoks, S. Poles, R. Purshouse, G. Rudolph, S. Ruzika, S. Sayìn, M.M. Wiecek, and X. Yao: Multiobjective Optimization for Interwoven Systems, pp. 139-151 in Dagstuhl Reports, Volume 5, Issue 1, 2015.
  12. J. Liu, D. Ashlock, G. Rudolph, C. F. Sironi, O. Teytaud, and M. Winan: Evolutionary Computation and Games, p. 83 in: J. Liu, T. Schaul, P. Spronck, and J. Togelius (eds.): Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI Dagstuhl Reports, Volume 9, Issue 12, 2020.
  13. S. Samothrakis, N. Nardelli, G. Rudolph, T. P. Rúnarsson, and T. Schaul: Black Swan AI, pp. 97-98 in: J. Liu, T. Schaul, P. Spronck, and J. Togelius (eds.): Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI Dagstuhl Reports, Volume 9, Issue 12, 2020.
  14. V. Volz, Y. Björnsson, M. Buro, R. D. Gaina, G. Rudolph, N. Sturtevant, and G. N. Yannakakis: Game-Playing Agent Evaluation, p. 110 in: J. Liu, T. Schaul, P. Spronck, and J. Togelius (eds.): Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI Dagstuhl Reports, Volume 9, Issue 12, 2020.
  1. M. Laumanns, G. Rudolph, and H.-P. Schwefel: Approximating the Pareto Set: Concepts, Diversity Issues, and Performance Assessment. Technical Report CI-72/99, University of Dortmund, March 1999 (ISSN 1433-3325).
  2. G. Rudolph: The Fundamental Matrix of the General Random Walk with Absorbing Boundaries. Technical Report CI-75/99, University of Dortmund, October 1999 (ISSN 1433-3325).
  3. L. Marti, C. Grimme, P. Kerschke, H. Trautmann, and G. Rudolph: Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms . Technical Report arXiv:1503.07845, Cornell University Library, March 2015.
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