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https://hdl.handle.net/2440/135706
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Type: | Conference paper |
Title: | Generating Instances with Performance Differences for More Than Just Two Algorithms |
Author: | Bossek, J. Wagner, M. |
Citation: | Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2021), 2021, vol.abs/2104.14275, pp.1423-1432 |
Publisher: | ACM |
Publisher Place: | New, NY, USA |
Issue Date: | 2021 |
ISBN: | 9781450383516 |
Conference Name: | Genetic and Evolutionary Computation Conference (GECCO) (10 Jul 2021 - 14 Jul 2021 : Lille, France) |
Statement of Responsibility: | Jakob Bossek, Markus Wagner |
Abstract: | 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. |
Keywords: | Evolutionary algorithms; evolving instances; traveling thief problem (TTP); fitness function; instance hardness |
Rights: | © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM |
DOI: | 10.1145/3449726.3463165 |
Grant ID: | http://purl.org/au-research/grants/arc/DP200102364 http://purl.org/au-research/grants/arc/DP210102670 |
Published version: | https://dl.acm.org/doi/proceedings/10.1145/3449726 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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