[{"doi":"10.1162/evco_a_00371","title":"MO-SMAC: Multi-objective Sequential Model-based Algorithm Configuration","date_created":"2025-03-21T06:24:46Z","author":[{"first_name":"Jeroen G.","full_name":"Rook, Jeroen G.","last_name":"Rook"},{"first_name":"Carolin","last_name":"Benjamins","full_name":"Benjamins, Carolin"},{"last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"first_name":"Heike","id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann"},{"full_name":"Hoos, Holger H.","last_name":"Hoos","first_name":"Holger H."},{"first_name":"Marius","last_name":"Lindauer","full_name":"Lindauer, Marius"}],"date_updated":"2025-03-21T06:25:31Z","citation":{"ama":"Rook JG, Benjamins C, Bossek J, Trautmann H, Hoos HH, Lindauer M. MO-SMAC: Multi-objective Sequential Model-based Algorithm Configuration. <i>Evolutionary Computation</i>. Published online 2025:1-25. doi:<a href=\"https://doi.org/10.1162/evco_a_00371\">10.1162/evco_a_00371</a>","chicago":"Rook, Jeroen G., Carolin Benjamins, Jakob Bossek, Heike Trautmann, Holger H. Hoos, and Marius Lindauer. “MO-SMAC: Multi-Objective Sequential Model-Based Algorithm Configuration.” <i>Evolutionary Computation</i>, 2025, 1–25. <a href=\"https://doi.org/10.1162/evco_a_00371\">https://doi.org/10.1162/evco_a_00371</a>.","ieee":"J. G. Rook, C. Benjamins, J. Bossek, H. Trautmann, H. H. Hoos, and M. Lindauer, “MO-SMAC: Multi-objective Sequential Model-based Algorithm Configuration,” <i>Evolutionary Computation</i>, pp. 1–25, 2025, doi: <a href=\"https://doi.org/10.1162/evco_a_00371\">10.1162/evco_a_00371</a>.","apa":"Rook, J. G., Benjamins, C., Bossek, J., Trautmann, H., Hoos, H. H., &#38; Lindauer, M. (2025). MO-SMAC: Multi-objective Sequential Model-based Algorithm Configuration. <i>Evolutionary Computation</i>, 1–25. <a href=\"https://doi.org/10.1162/evco_a_00371\">https://doi.org/10.1162/evco_a_00371</a>","bibtex":"@article{Rook_Benjamins_Bossek_Trautmann_Hoos_Lindauer_2025, title={MO-SMAC: Multi-objective Sequential Model-based Algorithm Configuration}, DOI={<a href=\"https://doi.org/10.1162/evco_a_00371\">10.1162/evco_a_00371</a>}, journal={Evolutionary Computation}, author={Rook, Jeroen G. and Benjamins, Carolin and Bossek, Jakob and Trautmann, Heike and Hoos, Holger H. and Lindauer, Marius}, year={2025}, pages={1–25} }","short":"J.G. Rook, C. Benjamins, J. Bossek, H. Trautmann, H.H. Hoos, M. Lindauer, Evolutionary Computation (2025) 1–25.","mla":"Rook, Jeroen G., et al. “MO-SMAC: Multi-Objective Sequential Model-Based Algorithm Configuration.” <i>Evolutionary Computation</i>, 2025, pp. 1–25, doi:<a href=\"https://doi.org/10.1162/evco_a_00371\">10.1162/evco_a_00371</a>."},"page":"1-25","year":"2025","publication_identifier":{"issn":["1063-6560"]},"language":[{"iso":"eng"}],"user_id":"15504","_id":"59073","status":"public","type":"journal_article","publication":"Evolutionary Computation"},{"doi":"10.1162/evco_a_00341","title":"Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python","author":[{"last_name":"Prager","full_name":"Prager, Raphael Patrick","first_name":"Raphael Patrick"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","id":"100740","full_name":"Trautmann, Heike"}],"date_created":"2023-08-04T07:01:33Z","date_updated":"2023-10-16T12:35:56Z","citation":{"ama":"Prager RP, Trautmann H. Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. <i>Evolutionary Computation</i>. Published online 2023:1–25. doi:<a href=\"https://doi.org/10.1162/evco_a_00341\">10.1162/evco_a_00341</a>","chicago":"Prager, Raphael Patrick, and Heike Trautmann. “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.” <i>Evolutionary Computation</i>, 2023, 1–25. <a href=\"https://doi.org/10.1162/evco_a_00341\">https://doi.org/10.1162/evco_a_00341</a>.","ieee":"R. P. Prager and H. Trautmann, “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python,” <i>Evolutionary Computation</i>, pp. 1–25, 2023, doi: <a href=\"https://doi.org/10.1162/evco_a_00341\">10.1162/evco_a_00341</a>.","short":"R.P. Prager, H. Trautmann, Evolutionary Computation (2023) 1–25.","mla":"Prager, Raphael Patrick, and Heike Trautmann. “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.” <i>Evolutionary Computation</i>, 2023, pp. 1–25, doi:<a href=\"https://doi.org/10.1162/evco_a_00341\">10.1162/evco_a_00341</a>.","bibtex":"@article{Prager_Trautmann_2023, title={Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python}, DOI={<a href=\"https://doi.org/10.1162/evco_a_00341\">10.1162/evco_a_00341</a>}, journal={Evolutionary Computation}, author={Prager, Raphael Patrick and Trautmann, Heike}, year={2023}, pages={1–25} }","apa":"Prager, R. P., &#38; Trautmann, H. (2023). Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. <i>Evolutionary Computation</i>, 1–25. <a href=\"https://doi.org/10.1162/evco_a_00341\">https://doi.org/10.1162/evco_a_00341</a>"},"page":"1–25","year":"2023","publication_identifier":{"issn":["1063-6560"]},"language":[{"iso":"eng"}],"user_id":"15504","department":[{"_id":"819"},{"_id":"34"}],"_id":"46299","status":"public","abstract":[{"lang":"eng","text":"The herein proposed Python package pflacco provides a set of numerical features to characterize single-objective continuous and constrained optimization problems. Thereby, pflacco addresses two major challenges in the area optimization. Firstly, it provides the means to develop an understanding of a given problem instance, which is crucial for designing, selecting, or configuring optimization algorithms in general. Secondly, these numerical features can be utilized in the research streams of automated algorithm selection and configuration. While the majority of these landscape features is already available in the R package flacco, our Python implementation offers these tools to an even wider audience and thereby promotes research interests and novel avenues in the area of optimization."}],"type":"journal_article","publication":"Evolutionary Computation"},{"status":"public","abstract":[{"lang":"eng","text":"We contribute to the efficient approximation of the Pareto-set for the classical NP-hard multi-objective minimum spanning tree problem (moMST) adopting evolutionary computation. More precisely, by building upon preliminary work, we analyse the neighborhood structure of Pareto-optimal spanning trees and design several highly biased sub-graph-based mutation operators founded on the gained insights. In a nutshell, these operators replace (un)connected sub-trees of candidate solutions with locally optimal sub-trees. The latter (biased) step is realized by applying Kruskal’s single-objective MST algorithm to a weighted sum scalarization of a sub-graph.We prove runtime complexity results for the introduced operators and investigate the desirable Pareto-beneficial property. This property states that mutants cannot be dominated by their parent. Moreover, we perform an extensive experimental benchmark study to showcase the operator’s practical suitability. Our results confirm that the subgraph based operators beat baseline algorithms from the literature even with severely restricted computational budget in terms of function evaluations on four different classes of complete graphs with different shapes of the Pareto-front."}],"type":"journal_article","publication":"Evolutionary Computation","language":[{"iso":"eng"}],"user_id":"102979","department":[{"_id":"819"}],"_id":"48859","citation":{"apa":"Bossek, J., &#38; Grimme, C. (2023). On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem. <i>Evolutionary Computation</i>, 1–35. <a href=\"https://doi.org/10.1162/evco_a_00335\">https://doi.org/10.1162/evco_a_00335</a>","mla":"Bossek, Jakob, and Christian Grimme. “On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem.” <i>Evolutionary Computation</i>, 2023, pp. 1–35, doi:<a href=\"https://doi.org/10.1162/evco_a_00335\">10.1162/evco_a_00335</a>.","bibtex":"@article{Bossek_Grimme_2023, title={On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem}, DOI={<a href=\"https://doi.org/10.1162/evco_a_00335\">10.1162/evco_a_00335</a>}, journal={Evolutionary Computation}, author={Bossek, Jakob and Grimme, Christian}, year={2023}, pages={1–35} }","short":"J. Bossek, C. Grimme, Evolutionary Computation (2023) 1–35.","ieee":"J. Bossek and C. Grimme, “On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem,” <i>Evolutionary Computation</i>, pp. 1–35, 2023, doi: <a href=\"https://doi.org/10.1162/evco_a_00335\">10.1162/evco_a_00335</a>.","chicago":"Bossek, Jakob, and Christian Grimme. “On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem.” <i>Evolutionary Computation</i>, 2023, 1–35. <a href=\"https://doi.org/10.1162/evco_a_00335\">https://doi.org/10.1162/evco_a_00335</a>.","ama":"Bossek J, Grimme C. On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem. <i>Evolutionary Computation</i>. Published online 2023:1–35. doi:<a href=\"https://doi.org/10.1162/evco_a_00335\">10.1162/evco_a_00335</a>"},"page":"1–35","year":"2023","publication_identifier":{"issn":["1063-6560"]},"doi":"10.1162/evco_a_00335","title":"On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem","author":[{"first_name":"Jakob","id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"}],"date_created":"2023-11-14T15:58:55Z","date_updated":"2023-12-13T10:51:42Z"},{"title":"Leveraging TSP Solver Complementarity through Machine Learning","doi":"10.1162/evco_a_00215","date_updated":"2023-12-13T10:51:26Z","date_created":"2023-11-14T15:58:58Z","author":[{"last_name":"Kerschke","full_name":"Kerschke, Pascal","first_name":"Pascal"},{"full_name":"Kotthoff, Lars","last_name":"Kotthoff","first_name":"Lars"},{"orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"first_name":"Holger H.","last_name":"Hoos","full_name":"Hoos, Holger H."},{"last_name":"Trautmann","full_name":"Trautmann, Heike","first_name":"Heike"}],"volume":26,"year":"2018","citation":{"apa":"Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., &#38; Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. <i>Evolutionary Computation</i>, <i>26</i>(4), 597–620. <a href=\"https://doi.org/10.1162/evco_a_00215\">https://doi.org/10.1162/evco_a_00215</a>","short":"P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary Computation 26 (2018) 597–620.","mla":"Kerschke, Pascal, et al. “Leveraging TSP Solver Complementarity through Machine Learning.” <i>Evolutionary Computation</i>, vol. 26, no. 4, 2018, pp. 597–620, doi:<a href=\"https://doi.org/10.1162/evco_a_00215\">10.1162/evco_a_00215</a>.","bibtex":"@article{Kerschke_Kotthoff_Bossek_Hoos_Trautmann_2018, title={Leveraging TSP Solver Complementarity through Machine Learning}, volume={26}, DOI={<a href=\"https://doi.org/10.1162/evco_a_00215\">10.1162/evco_a_00215</a>}, number={4}, journal={Evolutionary Computation}, author={Kerschke, Pascal and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}, year={2018}, pages={597–620} }","ama":"Kerschke P, Kotthoff L, Bossek J, Hoos HH, Trautmann H. Leveraging TSP Solver Complementarity through Machine Learning. <i>Evolutionary Computation</i>. 2018;26(4):597–620. doi:<a href=\"https://doi.org/10.1162/evco_a_00215\">10.1162/evco_a_00215</a>","chicago":"Kerschke, Pascal, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, and Heike Trautmann. “Leveraging TSP Solver Complementarity through Machine Learning.” <i>Evolutionary Computation</i> 26, no. 4 (2018): 597–620. <a href=\"https://doi.org/10.1162/evco_a_00215\">https://doi.org/10.1162/evco_a_00215</a>.","ieee":"P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging TSP Solver Complementarity through Machine Learning,” <i>Evolutionary Computation</i>, vol. 26, no. 4, pp. 597–620, 2018, doi: <a href=\"https://doi.org/10.1162/evco_a_00215\">10.1162/evco_a_00215</a>."},"page":"597–620","intvolume":"        26","publication_identifier":{"issn":["1063-6560"]},"issue":"4","keyword":["automated algorithm selection","machine learning.","performance modeling","Travelling Salesperson Problem"],"language":[{"iso":"eng"}],"_id":"48884","user_id":"102979","department":[{"_id":"819"}],"abstract":[{"lang":"eng","text":"The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard problems. Over the years, many different solution approaches and solvers have been developed. For the first time, we directly compare five state-of-the-art inexact solvers\\textemdash namely, LKH, EAX, restart variants of those, and MAOS\\textemdash on a large set of well-known benchmark instances and demonstrate complementary performance, in that different instances may be solved most effectively by different algorithms. We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a per-instance basis and thus achieves significantly improved performance compared to the single best solver, representing an advance in the state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors provides insight into what drives this performance improvement."}],"status":"public","type":"journal_article","publication":"Evolutionary Computation"},{"type":"journal_article","publication":"Evolutionary Computation","abstract":[{"text":"In this paper, two approaches for estimating the generation in which a multi-objective evolutionary algorithm (MOEA) shows statistically significant signs of convergence are introduced. A set-based perspective is taken where convergence is measured by performance indicators. The proposed techniques fulfill the requirements of proper statistical assessment on the one hand and efficient optimisation for real-world problems on the other hand. The first approach accounts for the stochastic nature of the MOEA by repeating the optimisation runs for increasing generation numbers and analysing the performance indicators using statistical tools. This technique results in a very robust offline procedure. Moreover, an online convergence detection method is introduced as well. This method automatically stops the MOEA when either the variance of the performance indicators falls below a specified threshold or a stagnation of their overall trend is detected. Both methods are analysed and compared for two MOEA and on different classes of benchmark functions. It is shown that the methods successfully operate on all stated problems needing less function evaluations while preserving good approximation quality at the same time.","lang":"eng"}],"status":"public","_id":"46418","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"language":[{"iso":"eng"}],"publication_identifier":{"issn":["1063-6560"]},"issue":"4","year":"2009","citation":{"ieee":"H. Trautmann, T. Wagner, B. Naujoks, M. Preuss, and J. Mehnen, “Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms,” <i>Evolutionary Computation</i>, vol. 17, no. 4, pp. 493–509, 2009, doi: <a href=\"https://doi.org/10.1162/evco.2009.17.4.17403\">10.1162/evco.2009.17.4.17403</a>.","chicago":"Trautmann, Heike, T. Wagner, B. Naujoks, M. Preuss, and J. Mehnen. “Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms.” <i>Evolutionary Computation</i> 17, no. 4 (2009): 493–509. <a href=\"https://doi.org/10.1162/evco.2009.17.4.17403\">https://doi.org/10.1162/evco.2009.17.4.17403</a>.","ama":"Trautmann H, Wagner T, Naujoks B, Preuss M, Mehnen J. Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms. <i>Evolutionary Computation</i>. 2009;17(4):493-509. doi:<a href=\"https://doi.org/10.1162/evco.2009.17.4.17403\">10.1162/evco.2009.17.4.17403</a>","apa":"Trautmann, H., Wagner, T., Naujoks, B., Preuss, M., &#38; Mehnen, J. (2009). Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms. <i>Evolutionary Computation</i>, <i>17</i>(4), 493–509. <a href=\"https://doi.org/10.1162/evco.2009.17.4.17403\">https://doi.org/10.1162/evco.2009.17.4.17403</a>","short":"H. Trautmann, T. Wagner, B. Naujoks, M. Preuss, J. Mehnen, Evolutionary Computation 17 (2009) 493–509.","bibtex":"@article{Trautmann_Wagner_Naujoks_Preuss_Mehnen_2009, title={Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms}, volume={17}, DOI={<a href=\"https://doi.org/10.1162/evco.2009.17.4.17403\">10.1162/evco.2009.17.4.17403</a>}, number={4}, journal={Evolutionary Computation}, author={Trautmann, Heike and Wagner, T. and Naujoks, B. and Preuss, M. and Mehnen, J.}, year={2009}, pages={493–509} }","mla":"Trautmann, Heike, et al. “Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms.” <i>Evolutionary Computation</i>, vol. 17, no. 4, 2009, pp. 493–509, doi:<a href=\"https://doi.org/10.1162/evco.2009.17.4.17403\">10.1162/evco.2009.17.4.17403</a>."},"page":"493-509","intvolume":"        17","date_updated":"2024-06-10T11:55:57Z","date_created":"2023-08-04T16:19:21Z","author":[{"full_name":"Trautmann, Heike","id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"},{"first_name":"T.","last_name":"Wagner","full_name":"Wagner, T."},{"last_name":"Naujoks","full_name":"Naujoks, B.","first_name":"B."},{"full_name":"Preuss, M.","last_name":"Preuss","first_name":"M."},{"last_name":"Mehnen","full_name":"Mehnen, J.","first_name":"J."}],"volume":17,"title":"Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms","doi":"10.1162/evco.2009.17.4.17403"}]