Publications

Aspar, P., Steinhoff, V., Schäpermeier, L., Kerschke, P., & Trautmann, H. (2022).
The Objective That Freed Me: A Multi-objective Local Search Approach for Continuous Single-objective Optimization.
Natural Computing, 1 - 15, Springer.
https://doi.org/10.1007/s11047-022-09919-w

Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022).
Process-Oriented Stream Classification Pipeline: A Literature Review.
Applied Sciences 2022, 12(18), 9094, MDPI.
https://doi.org/10.3390/app12189094

Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2022).
A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection.
Theoretical Computer Science, 123 - 145, Vol. 940 (Part B), Elsevier.
https://doi.org/10.1016/j.tcs.2022.10.019

Rodrigues, A., Kerschke, P., de B. Pereira, C. A., Trautmann, H., Wagner, C., Hellingrath, B., Polpo, A. (2022).
Estimation of Component Reliability From Superposed Renewal Processes by Means of Latent Variables
Computational Statistics 37, 355 - 379,  Springer.
https://doi.org/10.1007/s00180-021-01124-0

Schäpermeier, L., Grimme, C., & Kerschke, P. (2022).
Plotting Impossible? Surveying Visualization Methods for Continuous Multi-Objective Benchmark Problems.
IEEE Transactions on Evolutionary Computation, 1306 - 1320, Vol. 26 (6), IEEE.
https://doi.org/10.1109/TEVC.2022.3214894

Grimme, C., Kerschke, P., Aspar, P., Trautmann, H., Preuss, M., Deutz, A. H., Wang, H., Emmerich, M.
Peeking beyond peaks: Challenges and research potentials of continuous multimodal multiobjective optimization
Computers & Operations Research 2021(136), 105489
https://doi.org/10.1016/j.cor.2021.105489

Bossek, J., Kerschke, P., & Trautmann, H. (2020).
A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.
Applied Soft Computing, 2020(88), 105901.
https://doi.org/10.1016/j.asoc.2019.105901

Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., & Bischl, B. (2019).
OpenML: An R package to connect to the machine learning platform OpenML. Computational Statistics, 2019, 977–991.
https://doi.org/10.1007/s00180-017-0742-2

Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2019).
Automated Algorithm Selection: Survey and Perspectives.
Evolutionary Computation (ECJ), 27(1), 3–45.
https://doi.org/10.1162/evco_a_00242

Kerschke, P., & Trautmann, H. (2019).
Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning.
Evolutionary Computation (ECJ), 27(1), 99–127.
https://doi.org/10.1162/evco_a_00236

Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2019).
Search Dynamics on Multimodal Multi-Objective Problems.
Evolutionary Computation (ECJ), 27(4), 577–609.
https://doi.org/10.1162/evco_a_00234

Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018).
Leveraging TSP Solver Complementarity through Machine Learning.
Evolutionary Computation (ECJ), 26(4), 597–620.
https://doi.org/10.1162/evco_a_00215

Bischl, B., Kerschke, P., Kotthoff, L., Lindauer, M., Malitsky, Y., Fréchette, A., Hoos, H. H., Hutter, F., Leyton-Brown, K., Tierney, K., & Vanschoren, J. (2016).
ASlib: A Benchmark Library for Algorithm Selection.
Artificial Intelligence Journal, 237, 41–58.
http://dx.doi.org/10.1016/j.artint.2016.04.003

Liboschik, T., Kerschke, P., Fokianos, K., & Fried, R. (2016).
Modelling interventions in INGARCH processes.
International Journal of Computer Mathematics, 93(4), 640–657.
https://doi.org/10.1080/00207160.2014.949250

Brüggenjürgen, S., Schaaf, N., Kerschke, P., & Huber, M. F. (2022).
Mixture of Decision Trees for Interpretable Machine Learning.
In: Proceedings of the 21st IEEE International Conference on Machine Learning and Applications (ICMLA '22), Nassau, The Bahamas, IEEE.
(current status: accepted for publication / in press)
Preliminary version on ArXiv: https://doi.org/10.48550/arXiv.2211.14617

Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022).
BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems.
In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN XVII), Dortmund, Germany, 192 - 206, Springer.
https://link.springer.com/chapter/10.1007/978-3-031-14714-2_14

Prager, R. P., Seiler, M. V., Trautmann, H., & Kerschke, P. (2022).
Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods.
In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN XVII), Dortmund, Germany, 3 - 17, Springer.
https://link.springer.com/chapter/10.1007/978-3-031-14714-2_1

Schäpermeier, L., Grimme, C., & Kerschke, P. (2022).
MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes. 
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’22), Boston, MA, USA, 592 - 600, ACM. 
https://doi.org/10.1145/3512290.3528793

Schneider, L., Schäpermeier, L., Prager, R. P., Bischl, B., Trautmann, H., & Kerschke, P. (2022).
HPO $\times$ ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis.
In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN XVII), Dortmund, Germany, 575 - 589, Springer.
https://link.springer.com/chapter/10.1007/978-3-031-14714-2_40

Seiler, M. V., Prager, R. P., Kerschke, P., & Trautmann, H. (2022).  
A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. 
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’22), Boston, MA, USA, 657 - 665, ACM. 
https://doi.org/10.1145/3512290.3528834

Aspar, P., Kerschke, P., Steinhoff, V., Trautmann, H., & Grimme, C. (2021).
Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization.
In: Proceedings of the 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO), Shenzhen, China, 311 – 322. Springer. 
https://doi.org/10.1007/978-3-030-72062-9_25

Heins, J., Bossek, J., Pohl, J., Moritz Seiler, Trautmann, H., Kerschke, P. (2021).
On the potential of normalized TSP features for automated algorithm selection
In: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI), Dornbirn, Austria, 1 - 15, ACM.
https://doi.org/10.1145/3450218.3477308
* nominated for best paper award

Prager, R., Seiler, M., Trautmann, H., & Kerschke, P. (2021).
Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization
In: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA, IEEE.
https://doi.org/10.1109/SSCI50451.2021.9660174

Schäpermeier, L., Grimme, C., & Kerschke, P. (2021).
To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes.
In: Proceedings of the 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO), Shenzhen, China, 632 – 644, Springer. 
https://doi.org/10.1007/978-3-030-72062-9_50

Bossek, J., Casel, K., Kerschke, P., & Neumann, F. (2020).
The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20), Cancun, Mexico, 1286–1294, ACM.
https://doi.org/10.1145/3377930.3390243

Bossek, J., Doerr, C., & Kerschke, P. (2020).
Initial Design Strategies and their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20), Cancun, Mexico, 778–786, ACM.
https://doi.org/10.1145/3377930.3390155

Bossek, J., Doerr, C., Kerschke, P., Neumann, A., & Neumann, F. (2020).
Evolving Sampling Strategies for One-Shot Optimization Tasks.
In: Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands, 111–124, Springer.
https://doi.org/10.1007/978-3-030-58112-1_8

Bossek, J., Kerschke, P., & Trautmann, H. (2020).
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.
In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, 1–8, IEEE.
http://doi.org/10.1109/CEC48606.2020.9185613

Prager, R. P., Trautmann, H., Wang, H., Bäck, T. H. W., & Kerschke, P. (2020).
Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis.
In: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 996–1003, IEEE.
https://doi.org/10.1109/SSCI47803.2020.9308510

Schäpermeier, L., Grimme, C., & Kerschke, P. (2020).
One PLOT to Show Them All: Visualization of Efficient Sets in Multi-Objective Landscapes. In: Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands, 154–167, Springer.
https://doi.org/10.1007/978-3-030-58115-2_11

Seiler, M. V., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020).
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.
In: Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands, 48–64, Springer.
https://doi.org/10.1007/978-3-030-58112-1_4

Seiler, M. V., Trautmann, H., & Kerschke, P. (2020).
Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries.
In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 1–8, IEEE.
https://doi.org/10.1109/IJCNN48605.2020.9207338

Steinhoff, V., Kerschke, P., Aspar, P., Trautmann, H., & Grimme, C. (2020). Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent.
In: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 2445–2452, IEEE.
https://doi.org/10.1109/SSCI47803.2020.9308259

Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.
In: Proceedings of the 15th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV), Potsdam, Germany, 58–71, ACM.
https://doi.org/10.1145/3299904.3340307

Doerr, C., Dreo, J., & Kerschke, P. (2019).
Making a Case for (Hyper-)Parameter Tuning as Benchmark Problems.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19) Companion, Prague, Czech Republic, 1755–1764, ACM.
https://doi.org/10.1145/3319619.3326857

Grimme, C., Kerschke, P., Emmerich, M. T. M., Preuss, M., Deutz, A. H., & Trautmann, H. (2019).
Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization.
In: Proceedings of the International Global Optimization Workshop (LeGO 2018), Leiden, The Netherlands, 020052-1-020052-4.
https://doi.org/10.1063/1.5090019

Grimme, C., Kerschke, P., & Trautmann, H. (2019).
Multimodality in Multi-Objective Optimization — More Boon than Bane?.
In: Proceedings of the 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO), East Lansing, MI, USA, 126–138, Springer.
https://doi.org/10.1007/978-3-030-12598-1_11

Kerschke, P., & Preuss, M. (2019).
Exploratory Landscape Analysis.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19) Companion, Prague, Czech Republic, 1137–1155, ACM.
https://doi.org/10.1145/3319619.3323389

Rapin, J., Gallagher, M., Kerschke, P., Preuss, M., & Teytaud, O. (2019).
Exploring the MLDA Benchmark on the Nevergrad Platform.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19) Companion, Prague, Czech Republic, 1888–1896, ACM.
https://doi.org/10.1145/3319619.3326830

Volz, V., Naujoks, B., Kerschke, P., & Tušar, T. (2019).
Single- and Multi-Objective Game-Benchmarkfor Evolutionary Algorithms.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19), Prague, Czech Republic, 647–655, ACM.
https://doi.org/10.1145/3321707.3321805

Kerschke, P., Bossek, J., & Trautmann, H. (2018).
Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18) Companion, Kyoto, Japan, 1737–1744, ACM.
https://doi.org/10.1145/3205651.3208233

Pappa, G. L., Emmerich, M. T., Bazzan, A., Browne, W., Deb, K., Doerr, C., Ðurasević, M., Epitropakis, M. G., Haraldsson, S. O., Jakobovic, D., Kerschke, P., Krawiec, K., Lehre, P. K., Li, X., Lissovoi, A., Malo, P., Martí, L., Mei, Y., Merelo, J. J., Miller, J. F., Moraglio, A., Nebro, A. J., Nguyen, S., Ochoa, G., Oliveto, P., Picek, S., Pillay, N., Preuss, M., Schoenauer, M., Senkerik, R., Sinha, A., Shir, O., Sudholt, D., Whitley, D., Wineberg, M., Woodward, J., & Zhang, M. (2018).
Tutorials at PPSN 2018.
In: Auger, A., Fonseca, C. M., Lourenço, N., Machado, P., Paquete, L., & Whitley, D. (Eds.), Proceedings of International Conference on Parallel Problem Solving from Nature (PPSN XV), 477–489, Springer.
https://doi.org/10.1007/978-3-319-99259-4_38

Purshouse, R., Zarges, C., Cussat-Blanc, S., Epitropakis, M. G., Gallagher, M., Jansen, T., Kerschke, P., Li, X., Lobo, F. G., Miller, J., Oliveto, P. S., Preuss, M., Squillero, G., Tonda, A., Wagner, M., Weise, T., Wilson, D., Wróbel, B., & Zamuda, A. (2018).
Workshops at PPSN 2018.
In: Auger, A., Fonseca, C. M., Lourenço, N., Machado, P., Paquete, L., & Whitley, D. (Eds.), Proceedings of International Conference on Parallel Problem Solving from Nature (PPSN XV), 490–497, Springer.
https://doi.org/10.1007/978-3-319-99259-4_39

Hanster, C., & Kerschke, P. (2017).
flaccogui: Exploratory Landscape Analysis for Everyone.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '17) Companion, Berlin, Germany, 1215–1222.
https://doi.org/10.1145/3067695.3082477

Kerschke, P., & Grimme, C. (2017).
An Expedition to Multimodal Multi-Objective Optimization Landscapes.
In: Proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO), Münster, Germany, 329–343.
https://doi.org/10.1007/978-3-319-54157-0_23

Kerschke, P., & Preuss, M. (2017).
Exploratory Landscape Analysis: Advanced Tutorial at GECCO 2017.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '17) Companion, Berlin, Germany, 762–781.
https://doi.org/10.1145/3067695.3067696

Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2016).
Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '16), Denver, CO, USA, 229–236, ACM.
https://doi.org/doi.org/10.1145/2908812.2908845

Kerschke, P., & Trautmann, H. (2016).
The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems.
In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada, IEEE.
https://doi.org/10.1109/CEC.2016.7748359

Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2016).
Towards Analyzing Multimodality of Multiobjective Landscapes.
In: Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN XIV), Edinburgh, Scotland, 962–972, Springer.
https://doi.org/10.1007/978-3-319-45823-6_90
* Winner of the Best Paper Award

Chinnov, A., Kerschke, P., Meske, C., Stieglitz, S., & Trautmann, H. (2015).
An Overview of Topic Discovery in Twitter Communication through Social Media Analytics. In: Proceedings of the 20th Americas Conference on Information Systems (AMCIS '15), Puerto Rico, 1–10.
http://aisel.aisnet.org/

Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2015).
Detecting Funnel Structures by Means of Exploratory Landscape Analysis.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '15), Madrid, Spain, 265–272, ACM.
https://doi.org/10.1145/2739480.2754642

Kotthoff, L., Kerschke, P., Hoos, H. H., & Trautmann, H. (2015).
Improving the State of the Art in Inexact TSP Solving using Per-Instance Algorithm Selection.
In: Dhaenens, C., Jourdan, L., & Marmion, M.-E. (Eds.), Proceedings of the 9th International Conference on Learning and Intelligent Optimization (LION), 202–217, Springer.
https://doi.org/10.1007/978-3-319-19084-6_18

Martí, L., Grimme, C., Kerschke, P., Trautmann, H., & Rudolph, G. (2015).
Averaged Hausdorff Approximations of Pareto Fronts Based on Multiobjective Estimation of Distribution Algorithms.
In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '15) Companion, Madrid, Spain, 1427–1428, ACM.
https://doi.org/10.1145/2739482.2764631

Kerschke, P., Preuss, M., Hernández, C., Schütze, O., Sun, J.-Q., Grimme, C., Rudolph, G., Bischl, B., & Trautmann, H. (2014).
Cell Mapping Techniques for Exploratory Landscape Analysis.
In: Tantar, A.-A., Tantar, E., Sun, J.-Q., Zhang, W., Ding, Q., Schütze, O., Emmerich, M. T. M., Legrand, P., Del, M. P., & Coello Coello, C. A. (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, 115–131, Springer.
https://doi.org/10.1007/978-3-319-07494-8_9

Kerschke, P., & Grimme, C. (2021).
Lifting the Multimodality-Fog in Continuous Multi-objective Optimization.
In: Preuss, M., Epitropakis, M. G., Li, X., & Fieldsend, J. (Eds.),
Metaheuristics for Finding Multiple Solutions, 89 – 111, Springer.
https://link.springer.com/chapter/10.1007/978-3-030-79553-5_4

Kerschke, P., & Trautmann, H. (2019).
Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco. 
In: Bauer, N., Ickstadt, K., Lübke, K., Szepannek, G., Trautmann, H., & Vichi, M. (Eds.), Applications in Statistical Computing, 93–123. Springer.
https://doi.org/10.1007/978-3-030-25147-5_7

Rudolph, G., Kononova, A. V., Aguirre, H., Kerschke, P., Ochoa, G., & Tušar, T. (2022).
Proceedings of the International Conference on Parallel Problem Solving from Nature (PPSN XVII) Part I, Dortmund, Germany, Springer.
https://link.springer.com/book/10.1007/978-3-031-14714-2

Rudolph, G., Kononova, A. V., Aguirre, H., Kerschke, P., Ochoa, G., & Tušar, T. (2022).
Proceedings of the International Conference on Parallel Problem Solving from Nature (PPSN XVII) Part II, Dortmund, Germany, Springer.
https://link.springer.com/book/10.1007/978-3-031-14721-0

Bartz-Beielstein, T., Doerr, C., Bossek, J., Chandrasekaran, S., Eftimov, T., Fischbach, A., Kerschke, P., López-Ibáñez, M., Malan, K. M., Moore, J. H., Naujoks, B., Orzechowski, P., Volz, V., Wagner, M., & Weise, T. (2020).
Benchmarking in Optimization: Best Practice and Open Issues.
Preprint on arXiv.
https://arxiv.org/abs/2007.03488

Bossek, J., Kerschke, P., & Trautmann, H. (2020).
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.
Preprint on arXiv.
https://arxiv.org/abs/2005.13289

Steinhoff, V., Kerschke, P., & Grimme, C. (2020).
Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems. 
Preprint on arXiv.
https://arxiv.org/abs/2006.14423

Bossek, J., Kerschke, P., Neumann, A., Neumann, F., & Doerr, C. (2019).
One-Shot Decision-Making with and without Surrogates.
Preprint on arXiv.
https://arxiv.org/abs/1912.08956

Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., & Bischl, B. (2017).
OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML.
Preprint on arXiv.
https://arxiv.org/abs/1701.01293

Kerschke, P. (2017).
Automated and Feature-Based Problem Characterization and Algorithm Selection Through Machine Learning.
Dissertation / PhD Thesis at the University of Münster.
http://nbn-resolving.de/

Kerschke, P. (2017).
Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco.
Preprint on arXiv.
https://arxiv.org/abs/1708.05258

Bischl, B., Kerschke, P., Kotthoff, L., Lindauer, M. T., Malitsky, Y., Fréchette, A., Hoos, H. H., Hutter, F., Leyton-Brown, K., Tierney, K., & Vanschoren, J. (2015).
ASlib: A Benchmark Library for Algorithm Selection.
Preprint on arXiv.
https://arxiv.org/abs/1506.02465

Martí, L., Grimme, C., Kerschke, P., Trautmann, H., & Rudolph, G. (2015).
Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms.
Preprint on arXiv.
https://arxiv.org/abs/1503.07845