[{"publication_identifier":{"isbn":["978-1-4503-9237-2"]},"publication_status":"published","page":"186–194","citation":{"mla":"Bossek, Jakob, and Frank Neumann. “Exploring the Feature Space of TSP Instances Using Quality Diversity.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2022, pp. 186–194, doi:<a href=\"https://doi.org/10.1145/3512290.3528851\">10.1145/3512290.3528851</a>.","short":"J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2022, pp. 186–194.","bibtex":"@inproceedings{Bossek_Neumann_2022, place={New York, NY, USA}, series={GECCO ’22}, title={Exploring the Feature Space of TSP Instances Using Quality Diversity}, DOI={<a href=\"https://doi.org/10.1145/3512290.3528851\">10.1145/3512290.3528851</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Frank}, year={2022}, pages={186–194}, collection={GECCO ’22} }","apa":"Bossek, J., &#38; Neumann, F. (2022). Exploring the Feature Space of TSP Instances Using Quality Diversity. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 186–194. <a href=\"https://doi.org/10.1145/3512290.3528851\">https://doi.org/10.1145/3512290.3528851</a>","ama":"Bossek J, Neumann F. Exploring the Feature Space of TSP Instances Using Quality Diversity. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’22. Association for Computing Machinery; 2022:186–194. doi:<a href=\"https://doi.org/10.1145/3512290.3528851\">10.1145/3512290.3528851</a>","ieee":"J. Bossek and F. Neumann, “Exploring the Feature Space of TSP Instances Using Quality Diversity,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2022, pp. 186–194, doi: <a href=\"https://doi.org/10.1145/3512290.3528851\">10.1145/3512290.3528851</a>.","chicago":"Bossek, Jakob, and Frank Neumann. “Exploring the Feature Space of TSP Instances Using Quality Diversity.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 186–194. GECCO ’22. New York, NY, USA: Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3512290.3528851\">https://doi.org/10.1145/3512290.3528851</a>."},"place":"New York, NY, USA","author":[{"full_name":"Bossek, Jakob","id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}],"date_updated":"2023-12-13T10:45:56Z","doi":"10.1145/3512290.3528851","type":"conference","status":"public","department":[{"_id":"819"}],"user_id":"102979","series_title":"GECCO ’22","_id":"48861","extern":"1","year":"2022","date_created":"2023-11-14T15:58:55Z","publisher":"Association for Computing Machinery","title":"Exploring the Feature Space of TSP Instances Using Quality Diversity","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","abstract":[{"text":"Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem. A wide range of methods using evolutionary computation techniques has been introduced in recent years. With this paper, we contribute to this area of research by providing a new approach based on quality diversity (QD) that is able to explore the whole feature space. QD algorithms allow to create solutions of high quality within a given feature space by splitting it up into boxes and improving solution quality within each box. We use our QD approach for the generation of TSP instances to visualize and analyze the variety of instances differentiating various TSP solvers and compare it to instances generated by established approaches from the literature.","lang":"eng"}],"language":[{"iso":"eng"}],"keyword":["instance features","instance generation","quality diversity","TSP"]},{"department":[{"_id":"819"}],"user_id":"102979","_id":"48881","language":[{"iso":"eng"}],"extern":"1","keyword":["automated algorithm selection","graph theory","instance features","normalization","traveling salesperson problem (TSP)"],"publication":"Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","type":"book_chapter","status":"public","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) 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.","lang":"eng"}],"author":[{"last_name":"Heins","full_name":"Heins, Jonathan","first_name":"Jonathan"},{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob"},{"first_name":"Janina","last_name":"Pohl","full_name":"Pohl, Janina"},{"first_name":"Moritz","full_name":"Seiler, Moritz","last_name":"Seiler"},{"first_name":"Heike","full_name":"Trautmann, Heike","last_name":"Trautmann"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"}],"date_created":"2023-11-14T15:58:58Z","publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:47:23Z","title":"On the Potential of Normalized TSP Features for Automated Algorithm Selection","publication_identifier":{"isbn":["978-1-4503-8352-3"]},"page":"1–15","citation":{"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.","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.","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.","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.","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} }"},"year":"2021","place":"New York, NY, USA"},{"type":"conference","status":"public","_id":"48842","series_title":"FOGA ’19","user_id":"102979","department":[{"_id":"819"}],"extern":"1","publication_status":"published","publication_identifier":{"isbn":["978-1-4503-6254-2"]},"place":"New York, NY, USA","citation":{"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>.","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.","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} }","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>","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>","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>.","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>."},"page":"58–71","date_updated":"2023-12-13T10:42:57Z","author":[{"last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"last_name":"Neumann","full_name":"Neumann, Aneta","first_name":"Aneta"},{"last_name":"Wagner","full_name":"Wagner, Markus","first_name":"Markus"},{"first_name":"Frank","full_name":"Neumann, Frank","last_name":"Neumann"},{"first_name":"Heike","full_name":"Trautmann, Heike","last_name":"Trautmann"}],"doi":"10.1145/3299904.3340307","publication":"Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","abstract":[{"lang":"eng","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."}],"keyword":["benchmarking","instance features","optimization","problem generation","traveling salesperson problem"],"language":[{"iso":"eng"}],"year":"2019","publisher":"Association for Computing Machinery","date_created":"2023-11-14T15:58:52Z","title":"Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators"}]