[{"issue":"C","publication_identifier":{"issn":["1568-4946"]},"citation":{"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>.","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>.","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>","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>","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} }","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>.","short":"J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020)."},"intvolume":"        88","year":"2020","date_created":"2023-11-14T15:58:53Z","author":[{"last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"volume":88,"date_updated":"2023-12-13T10:52:17Z","doi":"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 \\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.","lang":"eng"}],"user_id":"102979","department":[{"_id":"819"}],"_id":"48848","language":[{"iso":"eng"}],"keyword":["Algorithm selection","Combinatorial optimization","Multi-objective optimization","Performance measurement","Traveling Salesperson Problem"]},{"date_updated":"2024-06-10T12:00:46Z","volume":88,"date_created":"2023-08-04T07:42:26Z","author":[{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"}],"title":"A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms","doi":"https://doi.org/10.1016/j.asoc.2019.105901","publication_identifier":{"issn":["1568-4946"]},"year":"2020","intvolume":"        88","page":"105901","citation":{"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>.","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>.","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>","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>","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} }","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>."},"_id":"46334","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","keyword":["Algorithm selection","Multi-objective optimization","Performance measurement","Combinatorial optimization","Traveling Salesperson Problem"],"language":[{"iso":"eng"}],"publication":"Applied Soft Computing","type":"journal_article","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 – 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."}],"status":"public"},{"abstract":[{"text":"We tackle a bi-objective dynamic orienteering problem where customer requests arise as time passes by. The goal is to minimize the tour length traveled by a single delivery vehicle while simultaneously keeping the number of dismissed dynamic customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm which is grounded on insights gained from a previous series of work on an a-posteriori version of the problem, where all request times are known in advance. In our experiments, we simulate different decision maker strategies and evaluate the development of the Pareto-front approximations on exemplary problem instances. It turns out, that despite severely reduced computational budget and no oracle-knowledge of request times the dynamic EMOA is capable of producing approximations which partially dominate the results of the a-posteriori EMOA and dynamic integer linear programming strategies.","lang":"eng"}],"publication":"Evolutionary Multi-Criterion Optimization (EMO)","keyword":["Combinatorial optimization","Dynamic optimization","Metaheuristics","Multi-objective optimization","Vehicle routing"],"language":[{"iso":"eng"}],"year":"2019","title":"Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm","publisher":"Springer International Publishing","date_created":"2023-11-14T15:58:52Z","editor":[{"last_name":"Deb","full_name":"Deb, Kalyanmoy","first_name":"Kalyanmoy"},{"first_name":"Erik","last_name":"Goodman","full_name":"Goodman, Erik"},{"last_name":"Coello Coello","full_name":"Coello Coello, Carlos A.","first_name":"Carlos A."},{"first_name":"Kathrin","last_name":"Klamroth","full_name":"Klamroth, Kathrin"},{"first_name":"Kaisa","full_name":"Miettinen, Kaisa","last_name":"Miettinen"},{"last_name":"Mostaghim","full_name":"Mostaghim, Sanaz","first_name":"Sanaz"},{"last_name":"Reed","full_name":"Reed, Patrick","first_name":"Patrick"}],"status":"public","type":"conference","extern":"1","_id":"48841","user_id":"102979","series_title":"Lecture Notes in Computer Science","department":[{"_id":"819"}],"place":"Cham","citation":{"apa":"Bossek, J., Grimme, C., Meisel, S., Rudolph, G., &#38; Trautmann, H. (2019). Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. In K. Deb, E. Goodman, C. A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, &#38; P. Reed (Eds.), <i>Evolutionary Multi-Criterion Optimization (EMO)</i> (pp. 516–528). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">https://doi.org/10.1007/978-3-030-12598-1_41</a>","short":"J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: K. Deb, E. Goodman, C.A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO), Springer International Publishing, Cham, 2019, pp. 516–528.","mla":"Bossek, Jakob, et al. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.” <i>Evolutionary Multi-Criterion Optimization (EMO)</i>, edited by Kalyanmoy Deb et al., Springer International Publishing, 2019, pp. 516–528, doi:<a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">10.1007/978-3-030-12598-1_41</a>.","bibtex":"@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">10.1007/978-3-030-12598-1_41</a>}, booktitle={Evolutionary Multi-Criterion Optimization (EMO)}, publisher={Springer International Publishing}, author={Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}, editor={Deb, Kalyanmoy and Goodman, Erik and Coello Coello, Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}, year={2019}, pages={516–528}, collection={Lecture Notes in Computer Science} }","ama":"Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. In: Deb K, Goodman E, Coello Coello CA, et al., eds. <i>Evolutionary Multi-Criterion Optimization (EMO)</i>. Lecture Notes in Computer Science. Springer International Publishing; 2019:516–528. doi:<a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">10.1007/978-3-030-12598-1_41</a>","ieee":"J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm,” in <i>Evolutionary Multi-Criterion Optimization (EMO)</i>, 2019, pp. 516–528, doi: <a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">10.1007/978-3-030-12598-1_41</a>.","chicago":"Bossek, Jakob, Christian Grimme, Stephan Meisel, Günter Rudolph, and Heike Trautmann. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.” In <i>Evolutionary Multi-Criterion Optimization (EMO)</i>, edited by Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, and Patrick Reed, 516–528. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019. <a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">https://doi.org/10.1007/978-3-030-12598-1_41</a>."},"page":"516–528","publication_status":"published","publication_identifier":{"isbn":["978-3-030-12598-1"]},"doi":"10.1007/978-3-030-12598-1_41","date_updated":"2023-12-13T10:43:07Z","author":[{"full_name":"Bossek, Jakob","id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"last_name":"Grimme","full_name":"Grimme, Christian","first_name":"Christian"},{"first_name":"Stephan","last_name":"Meisel","full_name":"Meisel, Stephan"},{"first_name":"Günter","last_name":"Rudolph","full_name":"Rudolph, Günter"},{"last_name":"Trautmann","full_name":"Trautmann, Heike","first_name":"Heike"}]},{"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","abstract":[{"text":"Research has shown that for many single-objective graph problems where optimum solutions are composed of low weight sub-graphs, such as the minimum spanning tree problem (MST), mutation operators favoring low weight edges show superior performance. Intuitively, similar observations should hold for multi-criteria variants of such problems. In this work, we focus on the multi-criteria MST problem. A thorough experimental study is conducted where we estimate the probability of edges being part of non-dominated spanning trees as a function of the edges’ non-domination level or domination count, respectively. Building on gained insights, we propose several biased one-edge-exchange mutation operators that differ in the used edge-selection probability distribution (biased towards edges of low rank). Our empirical analysis shows that among different graph types (dense and sparse) and edge weight types (both uniformly random and combinations of Euclidean and uniformly random) biased edge-selection strategies perform superior in contrast to the baseline uniform edge-selection. Our findings are in particular strong for dense graphs.","lang":"eng"}],"keyword":["biased mutation","combinatorial optimization","minimum spanning tree","multi-objective optimization"],"language":[{"iso":"eng"}],"year":"2019","publisher":"Association for Computing Machinery","date_created":"2023-11-14T15:58:52Z","title":"On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem","type":"conference","status":"public","_id":"48840","department":[{"_id":"819"}],"user_id":"102979","series_title":"GECCO ’19","extern":"1","publication_identifier":{"isbn":["978-1-4503-6111-8"]},"publication_status":"published","place":"New York, NY, USA","page":"516–523","citation":{"ama":"Bossek J, Grimme C, Neumann F. On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’19. Association for Computing Machinery; 2019:516–523. doi:<a href=\"https://doi.org/10.1145/3321707.3321818\">10.1145/3321707.3321818</a>","chicago":"Bossek, Jakob, Christian Grimme, and Frank Neumann. “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 516–523. GECCO ’19. New York, NY, USA: Association for Computing Machinery, 2019. <a href=\"https://doi.org/10.1145/3321707.3321818\">https://doi.org/10.1145/3321707.3321818</a>.","ieee":"J. Bossek, C. Grimme, and F. Neumann, “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2019, pp. 516–523, doi: <a href=\"https://doi.org/10.1145/3321707.3321818\">10.1145/3321707.3321818</a>.","apa":"Bossek, J., Grimme, C., &#38; Neumann, F. (2019). On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 516–523. <a href=\"https://doi.org/10.1145/3321707.3321818\">https://doi.org/10.1145/3321707.3321818</a>","short":"J. Bossek, C. Grimme, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2019, pp. 516–523.","mla":"Bossek, Jakob, et al. “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2019, pp. 516–523, doi:<a href=\"https://doi.org/10.1145/3321707.3321818\">10.1145/3321707.3321818</a>.","bibtex":"@inproceedings{Bossek_Grimme_Neumann_2019, place={New York, NY, USA}, series={GECCO ’19}, title={On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem}, DOI={<a href=\"https://doi.org/10.1145/3321707.3321818\">10.1145/3321707.3321818</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme, Christian and Neumann, Frank}, year={2019}, pages={516–523}, collection={GECCO ’19} }"},"date_updated":"2023-12-13T10:42:24Z","author":[{"orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979","first_name":"Jakob"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"last_name":"Neumann","full_name":"Neumann, Frank","first_name":"Frank"}],"doi":"10.1145/3321707.3321818"},{"year":"2018","title":"Local Search Effects in Bi-Objective Orienteering","date_created":"2023-11-14T15:58:51Z","publisher":"Association for Computing Machinery","abstract":[{"lang":"eng","text":"We analyze the effects of including local search techniques into a multi-objective evolutionary algorithm for solving a bi-objective orienteering problem with a single vehicle while the two conflicting objectives are minimization of travel time and maximization of the number of visited customer locations. Experiments are based on a large set of specifically designed problem instances with different characteristics and it is shown that local search techniques focusing on one of the objectives only improve the performance of the evolutionary algorithm in terms of both objectives. The analysis also shows that local search techniques are capable of sending locally optimal solutions to foremost fronts of the multi-objective optimization process, and that these solutions then become the leading factors of the evolutionary process."}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","language":[{"iso":"eng"}],"keyword":["combinatorial optimization","metaheuristics","multi-objective optimization","orienteering","transportation"],"page":"585–592","citation":{"ama":"Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Local Search Effects in Bi-Objective Orienteering. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’18. Association for Computing Machinery; 2018:585–592. doi:<a href=\"https://doi.org/10.1145/3205455.3205548\">10.1145/3205455.3205548</a>","chicago":"Bossek, Jakob, Christian Grimme, Stephan Meisel, Günter Rudolph, and Heike Trautmann. “Local Search Effects in Bi-Objective Orienteering.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 585–592. GECCO ’18. New York, NY, USA: Association for Computing Machinery, 2018. <a href=\"https://doi.org/10.1145/3205455.3205548\">https://doi.org/10.1145/3205455.3205548</a>.","ieee":"J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Local Search Effects in Bi-Objective Orienteering,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2018, pp. 585–592, doi: <a href=\"https://doi.org/10.1145/3205455.3205548\">10.1145/3205455.3205548</a>.","short":"J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2018, pp. 585–592.","mla":"Bossek, Jakob, et al. “Local Search Effects in Bi-Objective Orienteering.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2018, pp. 585–592, doi:<a href=\"https://doi.org/10.1145/3205455.3205548\">10.1145/3205455.3205548</a>.","bibtex":"@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2018, place={New York, NY, USA}, series={GECCO ’18}, title={Local Search Effects in Bi-Objective Orienteering}, DOI={<a href=\"https://doi.org/10.1145/3205455.3205548\">10.1145/3205455.3205548</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}, year={2018}, pages={585–592}, collection={GECCO ’18} }","apa":"Bossek, J., Grimme, C., Meisel, S., Rudolph, G., &#38; Trautmann, H. (2018). Local Search Effects in Bi-Objective Orienteering. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 585–592. <a href=\"https://doi.org/10.1145/3205455.3205548\">https://doi.org/10.1145/3205455.3205548</a>"},"place":"New York, NY, USA","publication_identifier":{"isbn":["978-1-4503-5618-3"]},"publication_status":"published","doi":"10.1145/3205455.3205548","author":[{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"first_name":"Stephan","last_name":"Meisel","full_name":"Meisel, Stephan"},{"first_name":"Günter","last_name":"Rudolph","full_name":"Rudolph, Günter"},{"first_name":"Heike","full_name":"Trautmann, Heike","last_name":"Trautmann"}],"date_updated":"2023-12-13T10:42:14Z","status":"public","type":"conference","extern":"1","department":[{"_id":"819"}],"user_id":"102979","series_title":"GECCO ’18","_id":"48839"},{"type":"conference","publication":"Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037","status":"public","abstract":[{"text":"State of the Art inexact solvers of the NP-hard Traveling Salesperson Problem TSP are known to mostly yield high-quality solutions in reasonable computation times. With the purpose of understanding different levels of instance difficulties, instances for the current State of the Art heuristic TSP solvers LKH+restart and EAX+restart are presented which are evolved using a sophisticated evolutionary algorithm. More specifically, the performance differences of the respective solvers are maximized resulting in instances which are easier to solve for one solver and much more difficult for the other. Focusing on both optimization directions, instance features are identified which characterize both types of instances and increase the understanding of solver performance differences.","lang":"eng"}],"series_title":"AI*IA 2016","user_id":"102979","_id":"48874","language":[{"iso":"eng"}],"extern":"1","keyword":["Combinatorial optimization","Instance hardness","Metaheuristics","Transportation","TSP"],"publication_status":"published","publication_identifier":{"isbn":["978-3-319-49129-5"]},"citation":{"apa":"Bossek, J., &#38; Trautmann, H. (2016). Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. <i>Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037</i>, 3–12. <a href=\"https://doi.org/10.1007/978-3-319-49130-1_1\">https://doi.org/10.1007/978-3-319-49130-1_1</a>","short":"J. Bossek, H. Trautmann, in: Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037, Springer-Verlag, Berlin, Heidelberg, 2016, pp. 3–12.","mla":"Bossek, Jakob, and Heike Trautmann. “Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.” <i>Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037</i>, Springer-Verlag, 2016, pp. 3–12, doi:<a href=\"https://doi.org/10.1007/978-3-319-49130-1_1\">10.1007/978-3-319-49130-1_1</a>.","bibtex":"@inproceedings{Bossek_Trautmann_2016, place={Berlin, Heidelberg}, series={AI*IA 2016}, title={Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference}, DOI={<a href=\"https://doi.org/10.1007/978-3-319-49130-1_1\">10.1007/978-3-319-49130-1_1</a>}, booktitle={Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037}, publisher={Springer-Verlag}, author={Bossek, Jakob and Trautmann, Heike}, year={2016}, pages={3–12}, collection={AI*IA 2016} }","ieee":"J. Bossek and H. Trautmann, “Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference,” in <i>Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037</i>, 2016, pp. 3–12, doi: <a href=\"https://doi.org/10.1007/978-3-319-49130-1_1\">10.1007/978-3-319-49130-1_1</a>.","chicago":"Bossek, Jakob, and Heike Trautmann. “Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.” In <i>Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037</i>, 3–12. AI*IA 2016. Berlin, Heidelberg: Springer-Verlag, 2016. <a href=\"https://doi.org/10.1007/978-3-319-49130-1_1\">https://doi.org/10.1007/978-3-319-49130-1_1</a>.","ama":"Bossek J, Trautmann H. Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. In: <i>Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037</i>. AI*IA 2016. Springer-Verlag; 2016:3–12. doi:<a href=\"https://doi.org/10.1007/978-3-319-49130-1_1\">10.1007/978-3-319-49130-1_1</a>"},"page":"3–12","year":"2016","place":"Berlin, Heidelberg","date_created":"2023-11-14T15:58:57Z","author":[{"first_name":"Jakob","id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek"},{"first_name":"Heike","full_name":"Trautmann, Heike","last_name":"Trautmann"}],"date_updated":"2023-12-13T10:47:11Z","publisher":"Springer-Verlag","doi":"10.1007/978-3-319-49130-1_1","title":"Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference"},{"_id":"48887","department":[{"_id":"819"}],"series_title":"GECCO’15","user_id":"102979","keyword":["combinatorial optimization","metaheuristics","multi-objective optimization","online algorithms","transportation"],"extern":"1","language":[{"iso":"eng"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference ","type":"conference","abstract":[{"text":"We evaluate the performance of a multi-objective evolutionary algorithm on a class of dynamic routing problems with a single vehicle. In particular we focus on relating algorithmic performance to the most prominent characteristics of problem instances. The routing problem considers two types of customers: mandatory customers must be visited whereas optional customers do not necessarily have to be visited. Moreover, mandatory customers are known prior to the start of the tour whereas optional customers request for service at later points in time with the vehicle already being on its way. The multi-objective optimization problem then results as maximizing the number of visited customers while simultaneously minimizing total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm aims at approximating the related Pareto set for specifically designed benchmarking instances differing in terms of number of customers, geographical layout, fraction of mandatory customers, and request times of optional customers. Conceptional and experimental comparisons to online heuristic procedures are provided.","lang":"eng"}],"status":"public","date_updated":"2023-12-13T10:49:06Z","publisher":"Association for Computing Machinery","author":[{"full_name":"Meisel, Stephan","last_name":"Meisel","first_name":"Stephan"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"first_name":"Martin","last_name":"Wölck","full_name":"Wölck, Martin"},{"first_name":"Günter","last_name":"Rudolph","full_name":"Rudolph, Günter"},{"first_name":"Heike","full_name":"Trautmann, Heike","last_name":"Trautmann"}],"date_created":"2023-11-14T15:58:59Z","title":"Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle","doi":"10.1145/2739480.2754705","publication_identifier":{"isbn":["978-1-4503-3472-3"]},"year":"2015","place":"New York, NY, USA","page":"425–432","citation":{"apa":"Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., &#38; Trautmann, H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. <i>Proceedings of the Genetic and Evolutionary Computation Conference </i>, 425–432. <a href=\"https://doi.org/10.1145/2739480.2754705\">https://doi.org/10.1145/2739480.2754705</a>","mla":"Meisel, Stephan, et al. “Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle.” <i>Proceedings of the Genetic and Evolutionary Computation Conference </i>, Association for Computing Machinery, 2015, pp. 425–432, doi:<a href=\"https://doi.org/10.1145/2739480.2754705\">10.1145/2739480.2754705</a>.","bibtex":"@inproceedings{Meisel_Grimme_Bossek_Wölck_Rudolph_Trautmann_2015, place={New York, NY, USA}, series={GECCO’15}, title={Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle}, DOI={<a href=\"https://doi.org/10.1145/2739480.2754705\">10.1145/2739480.2754705</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference }, publisher={Association for Computing Machinery}, author={Meisel, Stephan and Grimme, Christian and Bossek, Jakob and Wölck, Martin and Rudolph, Günter and Trautmann, Heike}, year={2015}, pages={425–432}, collection={GECCO’15} }","short":"S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference , Association for Computing Machinery, New York, NY, USA, 2015, pp. 425–432.","ama":"Meisel S, Grimme C, Bossek J, Wölck M, Rudolph G, Trautmann H. Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference </i>. GECCO’15. Association for Computing Machinery; 2015:425–432. doi:<a href=\"https://doi.org/10.1145/2739480.2754705\">10.1145/2739480.2754705</a>","chicago":"Meisel, Stephan, Christian Grimme, Jakob Bossek, Martin Wölck, Günter Rudolph, and Heike Trautmann. “Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference </i>, 425–432. GECCO’15. New York, NY, USA: Association for Computing Machinery, 2015. <a href=\"https://doi.org/10.1145/2739480.2754705\">https://doi.org/10.1145/2739480.2754705</a>.","ieee":"S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, and H. Trautmann, “Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference </i>, 2015, pp. 425–432, doi: <a href=\"https://doi.org/10.1145/2739480.2754705\">10.1145/2739480.2754705</a>."}}]