Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/127219
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Type: Conference paper
Title: Evolving diverse TSP instances by means of novel and creative mutation operators
Author: Bossek, J.
Kerschke, P.
Neumann, A.
Wagner, M.
Neumann, F.
Trautmann, H.
Citation: FOGA '19: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2019 / pp.58-71
Publisher: Association for Computing Machinery
Publisher Place: online
Issue Date: 2019
ISBN: 9781450362542
Conference Name: Foundations of Genetic Algorithms (FOGA) (26 Aug 2019 - 29 Aug 2019 : Potsdam, Germany)
Statement of
Responsibility: 
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.
RMID: 1000011415
DOI: 10.1145/3299904.3340307
Grant ID: http://purl.org/au-research/grants/arc/DP190103894
Published version: https://doi.org/10.1145/3299904
Appears in Collections:Computer Science publications

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