Abstract
In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with 1000 nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies.
Original language | English |
---|---|
Title of host publication | Parallel Problem Solving from Nature – PPSN XVI |
Subtitle of host publication | 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I |
Editors | Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, Heike Trautmann |
Pages | 48-64 |
Number of pages | 17 |
ISBN (Electronic) | 978-3-030-58112-1 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Event | 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020 - Virtual Event Duration: 5 Sept 2020 → 9 Sept 2020 Conference number: 16 |
Publication series
Name | Lecture Notes in Computer Science |
---|---|
Volume | 12269 |
Conference
Conference | 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020 |
---|---|
Abbreviated title | PPSN 2020 |
City | Virtual Event |
Period | 5/09/20 → 9/09/20 |