Exploring the feature space of TSP instances using quality diversity
Date
2022
Authors
Bossek, J.
Neumann, F.
Editors
Fieldsend, J.E.
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Conference paper
Citation
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'22), 2022 / Fieldsend, J.E. (ed./s), pp.186-194
Statement of Responsibility
Jakob Bossek, Frank Neumann
Conference Name
The Genetic and Evolutionary Computation Conference (GECCO) (9 Jul 2022 - 13 Jul 2022 : virtual online)
Abstract
Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem. A wide range of methods using evolutionary computation techniques has been introduced in recent years. With this paper, we contribute to this area of research by providing a new approach based on quality diversity (QD) that is able to explore the whole feature space. QD algorithms allow to create solutions of high quality within a given feature space by splitting it up into boxes and improving solution quality within each box. We use our QD approach for the generation of TSP instances to visualize and analyze the variety of instances differentiating various TSP solvers and compare it to instances generated by established approaches from the literature.
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© 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.