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|Title:||Evolving diverse TSP instances by means of novel and creative mutation operators|
|Citation:||FOGA '19: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2019 / pp.58-71|
|Publisher:||Association for Computing Machinery|
|Conference Name:||Foundations of Genetic Algorithms (FOGA) (26 Aug 2019 - 29 Aug 2019 : Potsdam, Germany)|
|Jakob Bossek, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann, Heike Trautmann|
|Abstract:||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.|
|Rights:||© 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.|
|Appears in Collections:||Computer Science publications|
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