[{"page":"248–256","citation":{"mla":"Bossek, Jakob, et al. “On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2023, pp. 248–256, doi:<a href=\"https://doi.org/10.1145/3583131.3590384\">10.1145/3583131.3590384</a>.","short":"J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2023, pp. 248–256.","bibtex":"@inproceedings{Bossek_Neumann_Neumann_2023, place={New York, NY, USA}, series={GECCO’23}, title={On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1145/3583131.3590384\">10.1145/3583131.3590384</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2023}, pages={248–256}, collection={GECCO’23} }","apa":"Bossek, J., Neumann, A., &#38; Neumann, F. (2023). On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 248–256. <a href=\"https://doi.org/10.1145/3583131.3590384\">https://doi.org/10.1145/3583131.3590384</a>","ieee":"J. Bossek, A. Neumann, and F. Neumann, “On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2023, pp. 248–256, doi: <a href=\"https://doi.org/10.1145/3583131.3590384\">10.1145/3583131.3590384</a>.","chicago":"Bossek, Jakob, Aneta Neumann, and Frank Neumann. “On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 248–256. GECCO’23. New York, NY, USA: Association for Computing Machinery, 2023. <a href=\"https://doi.org/10.1145/3583131.3590384\">https://doi.org/10.1145/3583131.3590384</a>.","ama":"Bossek J, Neumann A, Neumann F. On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO’23. Association for Computing Machinery; 2023:248–256. doi:<a href=\"https://doi.org/10.1145/3583131.3590384\">10.1145/3583131.3590384</a>"},"year":"2023","place":"New York, NY, USA","publication_identifier":{"isbn":["9798400701191"]},"doi":"10.1145/3583131.3590384","title":"On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem","author":[{"orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979","first_name":"Jakob"},{"first_name":"Aneta","full_name":"Neumann, Aneta","last_name":"Neumann"},{"last_name":"Neumann","full_name":"Neumann, Frank","first_name":"Frank"}],"date_created":"2023-11-14T15:58:56Z","publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:46:27Z","status":"public","abstract":[{"text":"Evolutionary algorithms have been shown to obtain good solutions for complex optimization problems in static and dynamic environments. It is important to understand the behaviour of evolutionary algorithms for complex optimization problems that also involve dynamic and/or stochastic components in a systematic way in order to further increase their applicability to real-world problems. We investigate the node weighted traveling salesperson problem (W-TSP), which provides an abstraction of a wide range of weighted TSP problems, in dynamic settings. In the dynamic setting of the problem, items that have to be collected as part of a TSP tour change over time. We first present a dynamic setup for the dynamic W-TSP parameterized by different types of changes that are applied to the set of items to be collected when traversing the tour. Our first experimental investigations study the impact of such changes on resulting optimized tours in order to provide structural insights of optimization solutions. Afterwards, we investigate simple mutation-based evolutionary algorithms and study the impact of the mutation operators and the use of populations with dealing with the dynamic changes to the node weights of the problem.","lang":"eng"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","type":"conference","language":[{"iso":"eng"}],"extern":"1","keyword":["dynamic optimization","evolutionary algorithms","re-optimization","weighted traveling salesperson problem"],"department":[{"_id":"819"}],"series_title":"GECCO’23","user_id":"102979","_id":"48869"},{"type":"journal_article","publication":"Theoretical Computer Science","abstract":[{"text":"Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) nearest neighbor relationships of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-nearest neighbor graphs (NNG) of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. A proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.","lang":"eng"}],"status":"public","_id":"46310","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"keyword":["Feature normalization","Algorithm selection","Traveling salesperson problem"],"language":[{"iso":"eng"}],"publication_identifier":{"issn":["0304-3975"]},"year":"2023","citation":{"apa":"Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., &#38; Kerschke, P. (2023). A study on the effects of normalized TSP features for automated algorithm selection. <i>Theoretical Computer Science</i>, <i>940</i>, 123–145. <a href=\"https://doi.org/10.1016/j.tcs.2022.10.019\">https://doi.org/10.1016/j.tcs.2022.10.019</a>","mla":"Heins, Jonathan, et al. “A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection.” <i>Theoretical Computer Science</i>, vol. 940, 2023, pp. 123–45, doi:<a href=\"https://doi.org/10.1016/j.tcs.2022.10.019\">https://doi.org/10.1016/j.tcs.2022.10.019</a>.","bibtex":"@article{Heins_Bossek_Pohl_Seiler_Trautmann_Kerschke_2023, title={A study on the effects of normalized TSP features for automated algorithm selection}, volume={940}, DOI={<a href=\"https://doi.org/10.1016/j.tcs.2022.10.019\">https://doi.org/10.1016/j.tcs.2022.10.019</a>}, journal={Theoretical Computer Science}, author={Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, year={2023}, pages={123–145} }","short":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, Theoretical Computer Science 940 (2023) 123–145.","ama":"Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. A study on the effects of normalized TSP features for automated algorithm selection. <i>Theoretical Computer Science</i>. 2023;940:123-145. doi:<a href=\"https://doi.org/10.1016/j.tcs.2022.10.019\">https://doi.org/10.1016/j.tcs.2022.10.019</a>","chicago":"Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection.” <i>Theoretical Computer Science</i> 940 (2023): 123–45. <a href=\"https://doi.org/10.1016/j.tcs.2022.10.019\">https://doi.org/10.1016/j.tcs.2022.10.019</a>.","ieee":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “A study on the effects of normalized TSP features for automated algorithm selection,” <i>Theoretical Computer Science</i>, vol. 940, pp. 123–145, 2023, doi: <a href=\"https://doi.org/10.1016/j.tcs.2022.10.019\">https://doi.org/10.1016/j.tcs.2022.10.019</a>."},"page":"123-145","intvolume":"       940","date_updated":"2024-06-10T11:57:21Z","author":[{"full_name":"Heins, Jonathan","last_name":"Heins","first_name":"Jonathan"},{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Pohl, Janina","last_name":"Pohl","first_name":"Janina"},{"first_name":"Moritz","last_name":"Seiler","full_name":"Seiler, Moritz","id":"105520"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740","full_name":"Trautmann, Heike"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"}],"date_created":"2023-08-04T07:18:38Z","volume":940,"title":"A study on the effects of normalized TSP features for automated algorithm selection","doi":"https://doi.org/10.1016/j.tcs.2022.10.019"},{"place":"New York, NY, USA","year":"2021","citation":{"ieee":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On the Potential of Normalized TSP Features for Automated Algorithm Selection,” in <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–15.","chicago":"Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” In <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 1–15. New York, NY, USA: Association for Computing Machinery, 2021.","ama":"Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. On the Potential of Normalized TSP Features for Automated Algorithm Selection. In: <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. Association for Computing Machinery; 2021:1–15.","apa":"Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., &#38; Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i> (pp. 1–15). Association for Computing Machinery.","bibtex":"@inbook{Heins_Bossek_Pohl_Seiler_Trautmann_Kerschke_2021, place={New York, NY, USA}, title={On the Potential of Normalized TSP Features for Automated Algorithm Selection}, booktitle={Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, year={2021}, pages={1–15} }","mla":"Heins, Jonathan, et al. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2021, pp. 1–15.","short":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, in: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–15."},"page":"1–15","publication_identifier":{"isbn":["978-1-4503-8352-3"]},"title":"On the Potential of Normalized TSP Features for Automated Algorithm Selection","publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:47:23Z","date_created":"2023-11-14T15:58:58Z","author":[{"full_name":"Heins, Jonathan","last_name":"Heins","first_name":"Jonathan"},{"orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979","first_name":"Jakob"},{"first_name":"Janina","full_name":"Pohl, Janina","last_name":"Pohl"},{"last_name":"Seiler","full_name":"Seiler, Moritz","first_name":"Moritz"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"},{"last_name":"Kerschke","full_name":"Kerschke, Pascal","first_name":"Pascal"}],"abstract":[{"lang":"eng","text":"Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. Eventually, a proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features."}],"status":"public","type":"book_chapter","publication":"Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","keyword":["automated algorithm selection","graph theory","instance features","normalization","traveling salesperson problem (TSP)"],"extern":"1","language":[{"iso":"eng"}],"_id":"48881","user_id":"102979","department":[{"_id":"819"}]},{"language":[{"iso":"eng"}],"extern":"1","keyword":["evolutionary algorithms","evolutionary diversity optimisation","high-order entropy","traveling salesperson problem"],"department":[{"_id":"819"}],"series_title":"GECCO’21","user_id":"102979","_id":"48893","status":"public","abstract":[{"text":"Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years. It allows practitioners to choose from a set of high-quality alternatives. In this paper, we employ a population diversity measure, called the high-order entropy measure, in an evolutionary algorithm to compute a diverse set of high-quality solutions for the Traveling Salesperson Problem. In contrast to previous studies, our approach allows diversifying segments of tours containing several edges based on the entropy measure. We examine the resulting evolutionary diversity optimisation approach precisely in terms of the final set of solutions and theoretical properties. Experimental results show significant improvements compared to a recently proposed edge-based diversity optimisation approach when working with a large population of solutions or long segments.","lang":"eng"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","type":"conference","doi":"10.1145/3449639.3459384","title":"Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem","author":[{"full_name":"Nikfarjam, Adel","last_name":"Nikfarjam","first_name":"Adel"},{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob"},{"first_name":"Aneta","last_name":"Neumann","full_name":"Neumann, Aneta"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"date_created":"2023-11-14T15:59:00Z","publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:50:06Z","page":"600–608","citation":{"ieee":"A. Nikfarjam, J. Bossek, A. Neumann, and F. Neumann, “Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021, pp. 600–608, doi: <a href=\"https://doi.org/10.1145/3449639.3459384\">10.1145/3449639.3459384</a>.","chicago":"Nikfarjam, Adel, Jakob Bossek, Aneta Neumann, and Frank Neumann. “Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 600–608. GECCO’21. New York, NY, USA: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3449639.3459384\">https://doi.org/10.1145/3449639.3459384</a>.","ama":"Nikfarjam A, Bossek J, Neumann A, Neumann F. Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO’21. Association for Computing Machinery; 2021:600–608. doi:<a href=\"https://doi.org/10.1145/3449639.3459384\">10.1145/3449639.3459384</a>","apa":"Nikfarjam, A., Bossek, J., Neumann, A., &#38; Neumann, F. (2021). Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 600–608. <a href=\"https://doi.org/10.1145/3449639.3459384\">https://doi.org/10.1145/3449639.3459384</a>","bibtex":"@inproceedings{Nikfarjam_Bossek_Neumann_Neumann_2021, place={New York, NY, USA}, series={GECCO’21}, title={Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1145/3449639.3459384\">10.1145/3449639.3459384</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Nikfarjam, Adel and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2021}, pages={600–608}, collection={GECCO’21} }","mla":"Nikfarjam, Adel, et al. “Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2021, pp. 600–608, doi:<a href=\"https://doi.org/10.1145/3449639.3459384\">10.1145/3449639.3459384</a>.","short":"A. Nikfarjam, J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 600–608."},"place":"New York, NY, USA","year":"2021","publication_identifier":{"isbn":["978-1-4503-8350-9"]}},{"keyword":["edge assembly crossover (EAX)","evolutionary algorithms","evolutionary diversity optimisation (EDO)","traveling salesperson problem (TSP)"],"extern":"1","language":[{"iso":"eng"}],"_id":"48892","department":[{"_id":"819"}],"user_id":"102979","abstract":[{"lang":"eng","text":"Evolutionary algorithms based on edge assembly crossover (EAX) constitute some of the best performing incomplete solvers for the well-known traveling salesperson problem (TSP). Often, it is desirable to compute not just a single solution for a given problem, but a diverse set of high quality solutions from which a decision maker can choose one for implementation. Currently, there are only a few approaches for computing a diverse solution set for the TSP. Furthermore, almost all of them assume that the optimal solution is known. In this paper, we introduce evolutionary diversity optimisation (EDO) approaches for the TSP that find a diverse set of tours when the optimal tour is known or unknown. We show how to adopt EAX to not only find a high-quality solution but also to maximise the diversity of the population. The resulting EAX-based EDO approach, termed EAX-EDO is capable of obtaining diverse high-quality tours when the optimal solution for the TSP is known or unknown. A comparison to existing approaches shows that they are clearly outperformed by EAX-EDO."}],"status":"public","publication":"Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms","type":"book_chapter","title":"Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation","publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:49:59Z","author":[{"first_name":"Adel","last_name":"Nikfarjam","full_name":"Nikfarjam, Adel"},{"first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979"},{"last_name":"Neumann","full_name":"Neumann, Aneta","first_name":"Aneta"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"date_created":"2023-11-14T15:59:00Z","place":"New York, NY, USA","year":"2021","page":"1–11","citation":{"bibtex":"@inbook{Nikfarjam_Bossek_Neumann_Neumann_2021, place={New York, NY, USA}, title={Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation}, booktitle={Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Nikfarjam, Adel and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2021}, pages={1–11} }","mla":"Nikfarjam, Adel, et al. “Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation.” <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2021, pp. 1–11.","short":"A. Nikfarjam, J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–11.","apa":"Nikfarjam, A., Bossek, J., Neumann, A., &#38; Neumann, F. (2021). Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation. In <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i> (pp. 1–11). Association for Computing Machinery.","ieee":"A. Nikfarjam, J. Bossek, A. Neumann, and F. Neumann, “Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation,” in <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i>, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–11.","chicago":"Nikfarjam, Adel, Jakob Bossek, Aneta Neumann, and Frank Neumann. “Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation.” In <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 1–11. New York, NY, USA: Association for Computing Machinery, 2021.","ama":"Nikfarjam A, Bossek J, Neumann A, Neumann F. Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation. In: <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i>. Association for Computing Machinery; 2021:1–11."},"publication_identifier":{"isbn":["978-1-4503-8352-3"]}},{"page":"48–64","citation":{"short":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: Parallel Problem Solving from {Nature} (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 48–64.","bibtex":"@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Berlin, Heidelberg}, title={Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>}, booktitle={Parallel Problem Solving from {Nature} (PPSN XVI)}, publisher={Springer-Verlag}, author={Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={48–64} }","mla":"Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, Springer-Verlag, 2020, pp. 48–64, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>.","apa":"Seiler, M., Pohl, J., Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 48–64. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>","ama":"Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In: <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>. Springer-Verlag; 2020:48–64. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>","chicago":"Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” In <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 48–64. Berlin, Heidelberg: Springer-Verlag, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>.","ieee":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem,” in <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 2020, pp. 48–64, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>."},"year":"2020","place":"Berlin, Heidelberg","publication_identifier":{"isbn":["978-3-030-58111-4"]},"doi":"10.1007/978-3-030-58112-1_4","title":"Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem","date_created":"2023-11-14T15:59:00Z","author":[{"full_name":"Seiler, Moritz","last_name":"Seiler","first_name":"Moritz"},{"full_name":"Pohl, Janina","last_name":"Pohl","first_name":"Janina"},{"full_name":"Bossek, Jakob","id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"date_updated":"2023-12-13T10:49:45Z","publisher":"Springer-Verlag","status":"public","abstract":[{"text":"In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies.","lang":"eng"}],"publication":"Parallel Problem Solving from {Nature} (PPSN XVI)","type":"conference","extern":"1","language":[{"iso":"eng"}],"keyword":["Automated algorithm selection","Deep learning","Feature-based approaches","Traveling Salesperson Problem"],"department":[{"_id":"819"}],"user_id":"102979","_id":"48897"},{"doi":"10.1016/j.asoc.2019.105901","title":"A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms","date_created":"2023-11-14T15:58:53Z","author":[{"first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"volume":88,"date_updated":"2023-12-13T10:52:17Z","citation":{"chicago":"Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i> 88, no. C (2020). <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","ieee":"J. Bossek, P. Kerschke, and H. Trautmann, “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms,” <i>Applied Soft Computing</i>, vol. 88, no. C, 2020, doi: <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>.","ama":"Bossek J, Kerschke P, Trautmann H. A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. <i>Applied Soft Computing</i>. 2020;88(C). doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>","mla":"Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i>, vol. 88, no. C, 2020, doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>.","bibtex":"@article{Bossek_Kerschke_Trautmann_2020, title={A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms}, volume={88}, DOI={<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>}, number={C}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020} }","short":"J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020).","apa":"Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. <i>Applied Soft Computing</i>, <i>88</i>(C). <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>"},"intvolume":"        88","year":"2020","issue":"C","publication_identifier":{"issn":["1568-4946"]},"language":[{"iso":"eng"}],"keyword":["Algorithm selection","Combinatorial optimization","Multi-objective optimization","Performance measurement","Traveling Salesperson Problem"],"user_id":"102979","department":[{"_id":"819"}],"_id":"48848","status":"public","abstract":[{"lang":"eng","text":"We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs \\textendash both to be minimized \\textendash is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers. \\textbullet The multi-objective perspective is naturally generalizable to multiple objectives. \\textbullet Proof of relationship between HV and the PAR in the considered bi-objective space. \\textbullet New insights into complementary behavior of stochastic optimization algorithms."}],"type":"journal_article","publication":"Applied Soft Computing"},{"publication_identifier":{"issn":["1568-4946"]},"citation":{"chicago":"Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i> 88 (2020): 105901. <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","ieee":"J. Bossek, P. Kerschke, and H. Trautmann, “A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms,” <i>Applied Soft Computing</i>, vol. 88, p. 105901, 2020, doi: <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","ama":"Bossek J, Kerschke P, Trautmann H. A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. <i>Applied Soft Computing</i>. 2020;88:105901. doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>","mla":"Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i>, vol. 88, 2020, p. 105901, doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","short":"J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020) 105901.","bibtex":"@article{Bossek_Kerschke_Trautmann_2020, title={A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms}, volume={88}, DOI={<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={105901} }","apa":"Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. <i>Applied Soft Computing</i>, <i>88</i>, 105901. <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>"},"intvolume":"        88","page":"105901","year":"2020","date_created":"2023-08-04T07:42:26Z","author":[{"orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"}],"volume":88,"date_updated":"2024-06-10T12:00:46Z","doi":"https://doi.org/10.1016/j.asoc.2019.105901","title":"A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms","type":"journal_article","publication":"Applied Soft Computing","status":"public","abstract":[{"text":"We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs – both to be minimized – is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers.","lang":"eng"}],"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46334","language":[{"iso":"eng"}],"keyword":["Algorithm selection","Multi-objective optimization","Performance measurement","Combinatorial optimization","Traveling Salesperson Problem"]},{"doi":"10.1145/3299904.3340307","date_updated":"2023-12-13T10:42:57Z","author":[{"full_name":"Bossek, Jakob","id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"full_name":"Neumann, Aneta","last_name":"Neumann","first_name":"Aneta"},{"first_name":"Markus","last_name":"Wagner","full_name":"Wagner, Markus"},{"first_name":"Frank","full_name":"Neumann, Frank","last_name":"Neumann"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"place":"New York, NY, USA","citation":{"apa":"Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., &#38; Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 58–71. <a href=\"https://doi.org/10.1145/3299904.3340307\">https://doi.org/10.1145/3299904.3340307</a>","mla":"Bossek, Jakob, et al. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.” <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2019, pp. 58–71, doi:<a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>.","bibtex":"@inproceedings{Bossek_Kerschke_Neumann_Wagner_Neumann_Trautmann_2019, place={New York, NY, USA}, series={FOGA ’19}, title={Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators}, DOI={<a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>}, booktitle={Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Kerschke, Pascal and Neumann, Aneta and Wagner, Markus and Neumann, Frank and Trautmann, Heike}, year={2019}, pages={58–71}, collection={FOGA ’19} }","short":"J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann, in: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2019, pp. 58–71.","ieee":"J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann, “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators,” in <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 2019, pp. 58–71, doi: <a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>.","chicago":"Bossek, Jakob, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann, and Heike Trautmann. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.” In <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 58–71. FOGA ’19. New York, NY, USA: Association for Computing Machinery, 2019. <a href=\"https://doi.org/10.1145/3299904.3340307\">https://doi.org/10.1145/3299904.3340307</a>.","ama":"Bossek J, Kerschke P, Neumann A, Wagner M, Neumann F, Trautmann H. Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In: <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. FOGA ’19. Association for Computing Machinery; 2019:58–71. doi:<a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>"},"page":"58–71","publication_status":"published","publication_identifier":{"isbn":["978-1-4503-6254-2"]},"extern":"1","_id":"48842","series_title":"FOGA ’19","user_id":"102979","department":[{"_id":"819"}],"status":"public","type":"conference","title":"Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators","publisher":"Association for Computing Machinery","date_created":"2023-11-14T15:58:52Z","year":"2019","keyword":["benchmarking","instance features","optimization","problem generation","traveling salesperson problem"],"language":[{"iso":"eng"}],"abstract":[{"text":"Evolutionary algorithms have successfully been applied to evolve problem instances that exhibit a significant difference in performance for a given algorithm or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP). Creating a large variety of instances is crucial for successful applications in the blooming field of algorithm selection. In this paper, we introduce new and creative mutation operators for evolving instances of the TSP. We show that adopting those operators in an evolutionary algorithm allows for the generation of benchmark sets with highly desirable properties: (1) novelty by clear visual distinction to established benchmark sets in the field, (2) visual and quantitative diversity in the space of TSP problem characteristics, and (3) significant performance differences with respect to the restart versions of heuristic state-of-the-art TSP solvers EAX and LKH. The important aspect of diversity is addressed and achieved solely by the proposed mutation operators and not enforced by explicit diversity preservation.","lang":"eng"}],"publication":"Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms"},{"language":[{"iso":"eng"}],"keyword":["approximation algorithms","local search","traveling salesperson problem","feature selection","prediction","classification"],"department":[{"_id":"34"},{"_id":"819"}],"series_title":"FOGA XII ’13","user_id":"15504","_id":"46388","status":"public","abstract":[{"text":"Understanding the behaviour of well-known algorithms for classical NP-hard optimisation problems is still a difficult task. With this paper, we contribute to this research direction and carry out a feature based comparison of local search and the well-known Christofides approximation algorithm for the Traveling Salesperson Problem. We use an evolutionary algorithm approach to construct easy and hard instances for the Christofides algorithm, where we measure hardness in terms of approximation ratio. Our results point out important features and lead to hard and easy instances for this famous algorithm. Furthermore, our cross-comparison gives new insights on the complementary benefits of the different approaches.","lang":"eng"}],"publication":"Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII","type":"conference","doi":"10.1145/2460239.2460253","title":"A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem","author":[{"first_name":"Samadhi","full_name":"Nallaperuma, Samadhi","last_name":"Nallaperuma"},{"first_name":"Markus","full_name":"Wagner, Markus","last_name":"Wagner"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"},{"last_name":"Bischl","full_name":"Bischl, Bernd","first_name":"Bernd"},{"first_name":"Olaf","full_name":"Mersmann, Olaf","last_name":"Mersmann"},{"first_name":"Heike","id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann"}],"date_created":"2023-08-04T15:42:03Z","publisher":"Association for Computing Machinery","date_updated":"2023-10-16T13:45:53Z","page":"147–160","citation":{"chicago":"Nallaperuma, Samadhi, Markus Wagner, Frank Neumann, Bernd Bischl, Olaf Mersmann, and Heike Trautmann. “A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem.” In <i>Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII</i>, 147–160. FOGA XII ’13. New York, NY, USA: Association for Computing Machinery, 2013. <a href=\"https://doi.org/10.1145/2460239.2460253\">https://doi.org/10.1145/2460239.2460253</a>.","ieee":"S. Nallaperuma, M. Wagner, F. Neumann, B. Bischl, O. Mersmann, and H. Trautmann, “A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem,” in <i>Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII</i>, 2013, pp. 147–160, doi: <a href=\"https://doi.org/10.1145/2460239.2460253\">10.1145/2460239.2460253</a>.","ama":"Nallaperuma S, Wagner M, Neumann F, Bischl B, Mersmann O, Trautmann H. A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem. In: <i>Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII</i>. FOGA XII ’13. Association for Computing Machinery; 2013:147–160. doi:<a href=\"https://doi.org/10.1145/2460239.2460253\">10.1145/2460239.2460253</a>","apa":"Nallaperuma, S., Wagner, M., Neumann, F., Bischl, B., Mersmann, O., &#38; Trautmann, H. (2013). A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem. <i>Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII</i>, 147–160. <a href=\"https://doi.org/10.1145/2460239.2460253\">https://doi.org/10.1145/2460239.2460253</a>","mla":"Nallaperuma, Samadhi, et al. “A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem.” <i>Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII</i>, Association for Computing Machinery, 2013, pp. 147–160, doi:<a href=\"https://doi.org/10.1145/2460239.2460253\">10.1145/2460239.2460253</a>.","bibtex":"@inproceedings{Nallaperuma_Wagner_Neumann_Bischl_Mersmann_Trautmann_2013, place={New York, NY, USA}, series={FOGA XII ’13}, title={A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1145/2460239.2460253\">10.1145/2460239.2460253</a>}, booktitle={Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII}, publisher={Association for Computing Machinery}, author={Nallaperuma, Samadhi and Wagner, Markus and Neumann, Frank and Bischl, Bernd and Mersmann, Olaf and Trautmann, Heike}, year={2013}, pages={147–160}, collection={FOGA XII ’13} }","short":"S. Nallaperuma, M. Wagner, F. Neumann, B. Bischl, O. Mersmann, H. Trautmann, in: Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII, Association for Computing Machinery, New York, NY, USA, 2013, pp. 147–160."},"place":"New York, NY, USA","year":"2013","publication_identifier":{"isbn":["9781450319904"]}}]