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Under continuous construction!

Publications

  • Books

    1. G. Rudolph: Convergence Properties of Evolutionary Algorithms, Hamburg: Kovac 1997, ISBN 3-86064-554-4.

  • Edited Books

    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.
    10. G. Rudolph, A. V. Kononova, H. E. Aguirre, P. Kerschke, G. Ochoa, T. Tusar (eds.): Proceedings of the 17th International Conference on Parallel Problem Solving from Nature - PPSN XVII, Lecture Notes in Computer Science Vol. 13898 & 13899, Springer: Berlin and Heidelberg 2022.

  • Edited Special Issues in Journals

    1. A.E. Eiben and G. Rudolph (guest eds.): Special issue on "Theory of Evolutionary Algorithms". Theoretical Computer Science 229(1), 1999.
    2. M. Laumanns, S. Mostaghim, G. Rudolph, and J. Teich (guest eds.): Special issue on "Evolutionary Multiobjective Optimization". International Journal of Computational Intelligence Research 2(3), 2006.
    3. O. Kramer, C. Igel, and G. Rudolph (guest eds.): Special issue on "Evolutionary Kernel Machines". Evolutionary Intelligence 5(3), 2012.
    4. S. Greco, K. Klamroth, J. Knowles, and G. Rudolph (guest eds.): Special issue in "Understanding Complexity in Multiobjective Optimization". Journal of Multi-Criteria Decision Analysis 24(1/2), 2017.

  • Journals

    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 http://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: http://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 26(6):1365-1379, 2022.

  • Contributions to edited works (not available online, sorry!)

    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.

  • Proceedings

    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, P. Brinkmann, and G. Rudolph: Real-Time Object Detection with Intel NCS2 on Hardware with Limited Resources for Low-power IoT Devices, pp. 110-118 in D.Bastieri et al. (eds.): Proceedings of 7th Int'l Conf. on Internet of Things, Big Data and Security (IoTBDS 2022), SCITEPRESS, 2022. (doi: 10.5220/0010979900003194)
    119. 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), pp. 94-101 in Ana L. N. Fred et al. (eds.): Proceedings of 3rd International Conference on Deep Learning Theory and Applications (DeLTA 2022), SCITEPRESS 2022.
    120. F. Ostermann, I. Vatolkin, and G. Rudolph: Artificial Music Producer: Filtering Music Compositions by Artificial Taste, in Proceedings of the 3rd Conference on AI Music Creativity (AIMC), 2022. (doi: 10.5281/zenodo.7088395)
    121. G.Rudolph: Runtime Analysis of (1+1)-EA on a Biobjective Test Function in Unbounded Integer Search Space, pp. 1380-1385 in: Proceedings of 2023 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE Press, 2023. (doi: 10.1109/SSCI52147.2023.10371816)
    122. J. Kuzmic, F. Ostermann, and G. Rudolph: Emergency Corridor Building on Multi-Lane Motorways with Autonomous Model Cars, pp. 321-328 in Proceedings of the 9th Int'l Conf. on Internet of Things, Big Data and Security (IoTBDS 2024), SCITEPRESS, 2024. (doi: 10.5220/0012731700003705)
    123. G. Rudolph and M. Wagner: Towards Adaptation in Multiobjective Evolutionary Algorithms for Integer Problems, pp. 1-8 in 2024 IEEE Congress on Evolutionary Computation (CEC), IEEE Press, 2024. (doi: 10.1109/CEC60901.2024.10612114)
    124. F. Neumann and G. Rudolph: Archive-based Single-Objective Evolutionary Algorithms for Submodular Optimization, pp. 166–180 in M. Affenzeller et al. (eds.): 18th International Conference on Parallel Problem Solving from Nature (PPSN 2024), Springer: Cham 2024. (doi: 10.1007/978-3-031-70071-2_11)
    125. B. Doerr, M.S. Krejca, and G. Rudolph: Runtime Analysis for Multi-Objective Evolutionary Algorithms in Unbounded Integer Spaces, accepted for presentation at 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025), Philadelphia (USA), 27 February - 2 March 2025.
    126. G. Rudolph and M. Wagner: Cumulative Step Size Adaptation for Adaptive SEMO in Integer Space, accepted for presentation at 13th Int'l Conf. on Evolutionary Multi-Criterion Optimization (EMO 2025), Canberra (Australia), 4-7 March 2025.

  • Work currently under review

    some

  • Miscellaneous

    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.
      Available as Technical Report.
    8. 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.
    9. M. Preuss and G. Rudolph: Conference Report on IEEE CIG 2014. IEEE Computational Intelligence Magazine 10(1):14-15, 2015.
    10. 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.
    11. 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.
    12. 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.
    13. 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.

  • Unpublished

    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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