[{"abstract":[{"text":"The $$\\textbackslash mathcal NP$$-hard multi-criteria shortest path problem (mcSPP) is of utmost practical relevance, e.~g., in navigation system design and logistics. We address the problem of approximating the Pareto-front of the mcSPP with sum objectives. We do so by proposing a new mutation operator for multi-objective evolutionary algorithms that solves single-objective versions of the shortest path problem on subgraphs. A rigorous empirical benchmark on a diverse set of problem instances shows the effectiveness of the approach in comparison to a well-known mutation operator in terms of convergence speed and approximation quality. In addition, we glance at the neighbourhood structure and similarity of obtained Pareto-optimal solutions and derive promising directions for future work.","lang":"eng"}],"publication":"Learning and Intelligent Optimization","language":[{"iso":"eng"}],"year":"2019","title":"Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems","date_created":"2023-11-14T15:58:54Z","publisher":"Springer International Publishing","status":"public","editor":[{"first_name":"Roberto","full_name":"Battiti, Roberto","last_name":"Battiti"},{"full_name":"Brunato, Mauro","last_name":"Brunato","first_name":"Mauro"},{"full_name":"Kotsireas, Ilias","last_name":"Kotsireas","first_name":"Ilias"},{"full_name":"Pardalos, Panos M.","last_name":"Pardalos","first_name":"Panos M."}],"type":"conference","extern":"1","series_title":"Lecture Notes in Computer Science","user_id":"102979","department":[{"_id":"819"}],"_id":"48858","citation":{"apa":"Bossek, J., &#38; Grimme, C. (2019). Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems. In R. Battiti, M. Brunato, I. Kotsireas, &#38; P. M. Pardalos (Eds.), <i>Learning and Intelligent Optimization</i> (pp. 184–198). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">https://doi.org/10.1007/978-3-030-05348-2_17</a>","mla":"Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti et al., Springer International Publishing, 2019, pp. 184–198, doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">10.1007/978-3-030-05348-2_17</a>.","short":"J. Bossek, C. Grimme, in: R. Battiti, M. Brunato, I. Kotsireas, P.M. Pardalos (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2019, pp. 184–198.","bibtex":"@inproceedings{Bossek_Grimme_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">10.1007/978-3-030-05348-2_17</a>}, booktitle={Learning and Intelligent Optimization}, publisher={Springer International Publishing}, author={Bossek, Jakob and Grimme, Christian}, editor={Battiti, Roberto and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}, year={2019}, pages={184–198}, collection={Lecture Notes in Computer Science} }","ama":"Bossek J, Grimme C. Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems. In: Battiti R, Brunato M, Kotsireas I, Pardalos PM, eds. <i>Learning and Intelligent Optimization</i>. Lecture Notes in Computer Science. Springer International Publishing; 2019:184–198. doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">10.1007/978-3-030-05348-2_17</a>","chicago":"Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” In <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti, Mauro Brunato, Ilias Kotsireas, and Panos M. Pardalos, 184–198. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">https://doi.org/10.1007/978-3-030-05348-2_17</a>.","ieee":"J. Bossek and C. Grimme, “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems,” in <i>Learning and Intelligent Optimization</i>, 2019, pp. 184–198, doi: <a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">10.1007/978-3-030-05348-2_17</a>."},"page":"184–198","place":"Cham","publication_status":"published","publication_identifier":{"isbn":["978-3-030-05348-2"]},"doi":"10.1007/978-3-030-05348-2_17","author":[{"full_name":"Bossek, Jakob","id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"}],"date_updated":"2023-12-13T10:44:44Z"},{"publication":"Learning and Intelligent Optimization","abstract":[{"lang":"eng","text":"A multiobjective perspective onto common performance measures such as the PAR10 score or the expected runtime of single-objective stochastic solvers is presented by directly investigating the tradeoff between the fraction of failed runs and the average runtime. Multi-objective indicators operating in the bi-objective space allow for an overall performance comparison on a set of instances paving the way for instance-based automated algorithm selection techniques."}],"language":[{"iso":"eng"}],"keyword":["Algorithm selection","Performance measurement"],"year":"2019","date_created":"2023-11-14T15:58:57Z","publisher":"Springer International Publishing","title":"Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time","type":"conference","status":"public","editor":[{"full_name":"Battiti, Roberto","last_name":"Battiti","first_name":"Roberto"},{"first_name":"Mauro","full_name":"Brunato, Mauro","last_name":"Brunato"},{"last_name":"Kotsireas","full_name":"Kotsireas, Ilias","first_name":"Ilias"},{"first_name":"Panos M.","last_name":"Pardalos","full_name":"Pardalos, Panos M."}],"user_id":"102979","series_title":"Lecture Notes in Computer Science","department":[{"_id":"819"}],"_id":"48875","extern":"1","publication_identifier":{"isbn":["978-3-030-05348-2"]},"citation":{"mla":"Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti et al., Springer International Publishing, 2019, pp. 215–219, doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>.","bibtex":"@inproceedings{Bossek_Trautmann_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>}, booktitle={Learning and Intelligent Optimization}, publisher={Springer International Publishing}, author={Bossek, Jakob and Trautmann, Heike}, editor={Battiti, Roberto and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}, year={2019}, pages={215–219}, collection={Lecture Notes in Computer Science} }","short":"J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P.M. Pardalos (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2019, pp. 215–219.","apa":"Bossek, J., &#38; Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, &#38; P. M. Pardalos (Eds.), <i>Learning and Intelligent Optimization</i> (pp. 215–219). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">https://doi.org/10.1007/978-3-030-05348-2_19</a>","ama":"Bossek J, Trautmann H. Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In: Battiti R, Brunato M, Kotsireas I, Pardalos PM, eds. <i>Learning and Intelligent Optimization</i>. Lecture Notes in Computer Science. Springer International Publishing; 2019:215–219. doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>","ieee":"J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time,” in <i>Learning and Intelligent Optimization</i>, 2019, pp. 215–219, doi: <a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>.","chicago":"Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” In <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti, Mauro Brunato, Ilias Kotsireas, and Panos M. Pardalos, 215–219. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">https://doi.org/10.1007/978-3-030-05348-2_19</a>."},"page":"215–219","place":"Cham","author":[{"first_name":"Jakob","id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"date_updated":"2023-12-13T10:47:32Z","doi":"10.1007/978-3-030-05348-2_19"}]