{"keyword":["evolutionary algorithms","evolving instances","fitness function","instance hardness","traveling thief problem (TTP)"],"status":"public","language":[{"iso":"eng"}],"page":"1423–1432","type":"conference","user_id":"102979","doi":"10.1145/3449726.3463165","place":"New York, NY, USA","_id":"48876","date_updated":"2023-12-13T10:47:41Z","publication_identifier":{"isbn":["978-1-4503-8351-6"]},"abstract":[{"lang":"eng","text":"In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa. Typically, this is achieved by either minimizing or maximizing the performance difference or ratio which serves as the fitness function. Repeating this process is useful to gain insights into strengths/weaknesses of certain algorithms or to build a set of instances with strong performance differences as a foundation for automatic per-instance algorithm selection or configuration. We contribute to this branch of research by proposing fitness-functions to evolve instances that show large performance differences for more than just two algorithms simultaneously. As a proof-of-principle, we evolve instances of the multi-component Traveling Thief Problem (TTP) for three incomplete TTP-solvers. Our results point out that our strategies are promising, but unsurprisingly their success strongly relies on the algorithms’ performance complementarity."}],"title":"Generating Instances with Performance Differences for More than Just Two Algorithms","publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","year":"2021","author":[{"last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","first_name":"Jakob","id":"102979"},{"last_name":"Wagner","full_name":"Wagner, Markus","first_name":"Markus"}],"department":[{"_id":"819"}],"publisher":"Association for Computing Machinery","date_created":"2023-11-14T15:58:57Z","series_title":"GECCO’21","extern":"1","citation":{"ieee":"J. Bossek and M. Wagner, “Generating Instances with Performance Differences for More than Just Two Algorithms,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021, pp. 1423–1432, doi: 10.1145/3449726.3463165.","short":"J. Bossek, M. Wagner, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1423–1432.","bibtex":"@inproceedings{Bossek_Wagner_2021, place={New York, NY, USA}, series={GECCO’21}, title={Generating Instances with Performance Differences for More than Just Two Algorithms}, DOI={10.1145/3449726.3463165}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Wagner, Markus}, year={2021}, pages={1423–1432}, collection={GECCO’21} }","ama":"Bossek J, Wagner M. Generating Instances with Performance Differences for More than Just Two Algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO’21. Association for Computing Machinery; 2021:1423–1432. doi:10.1145/3449726.3463165","chicago":"Bossek, Jakob, and Markus Wagner. “Generating Instances with Performance Differences for More than Just Two Algorithms.” In Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1423–1432. GECCO’21. New York, NY, USA: Association for Computing Machinery, 2021. https://doi.org/10.1145/3449726.3463165.","apa":"Bossek, J., & Wagner, M. (2021). Generating Instances with Performance Differences for More than Just Two Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1423–1432. https://doi.org/10.1145/3449726.3463165","mla":"Bossek, Jakob, and Markus Wagner. “Generating Instances with Performance Differences for More than Just Two Algorithms.” Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, 2021, pp. 1423–1432, doi:10.1145/3449726.3463165."}}