research-article

Exploring the feature space of TSP instances using quality diversity

Authors:

Frank NeumannAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 186 - 194

https://doi.org/10.1145/3512290.3528851

Published: 08 July 2022 Publication History

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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Cited By

Smith-Miles K(2025)Understanding Instance Hardness for Optimisation Algorithms: Methodologies, Open Challenges and Post-Quantum ImplicationsApplied Mathematical Modelling10.1016/j.apm.2025.115965(115965)Online publication date: Feb-2025
https://doi.org/10.1016/j.apm.2025.115965
Bossek JSudholt D(2024)Runtime Analysis of Quality Diversity AlgorithmsAlgorithmica10.1007/s00453-024-01254-z86:10(3252-3283)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00453-024-01254-z
Asín‐Achá REspinoza AGoldschmidt OHochbaum DHuerta I(2024)Selecting fast algorithms for the capacitated vehicle routing problem with machine learning techniquesNetworks10.1002/net.2224484:4(465-480)Online publication date: 24-Jul-2024
https://doi.org/10.1002/net.22244
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Index Terms

Exploring the feature space of TSP instances using quality diversity
1. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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cover image ACM Conferences

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference

July 2022

1472 pages

ISBN:9781450392372

DOI:10.1145/3512290

Editor:
Jonathan E. Fieldsend
University of Exeter
,
General Chair:
Markus Wagner
The University of Adelaide

Copyright © 2022 ACM.

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Published: 08 July 2022

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Australian Research Council (ARC)

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GECCO '22

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SIGEVO

GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Smith-Miles K(2025)Understanding Instance Hardness for Optimisation Algorithms: Methodologies, Open Challenges and Post-Quantum ImplicationsApplied Mathematical Modelling10.1016/j.apm.2025.115965(115965)Online publication date: Feb-2025
https://doi.org/10.1016/j.apm.2025.115965
Bossek JSudholt D(2024)Runtime Analysis of Quality Diversity AlgorithmsAlgorithmica10.1007/s00453-024-01254-z86:10(3252-3283)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00453-024-01254-z
Asín‐Achá REspinoza AGoldschmidt OHochbaum DHuerta I(2024)Selecting fast algorithms for the capacitated vehicle routing problem with machine learning techniquesNetworks10.1002/net.2224484:4(465-480)Online publication date: 24-Jul-2024
https://doi.org/10.1002/net.22244
Hagg AKliemank MAsteroth AWilde DBedrunka MFoysi HReith D(2023)Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-OptimizationEvolutionary Computation10.1162/evco_a_0032631:3(287-307)Online publication date: 1-Sep-2023
https://doi.org/10.1162/evco_a_00326
Nikfarjam ARothenberger RNeumann FFriedrich TSilva SPaquete L(2023)Evolutionary Diversity Optimisation in Constructing Satisfying AssignmentsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590517(938-945)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590517
Marrero ASegredo EHart EBossek JNeumann ASilva SPaquete L(2023)Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-spaceProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590504(312-320)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590504
Bossek JSudholt DSilva SPaquete L(2023)Runtime Analysis of Quality Diversity AlgorithmsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590383(1546-1554)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590383
Heins JBossek JPohl JSeiler MTrautmann HKerschke P(2023)A study on the effects of normalized TSP features for automated algorithm selectionTheoretical Computer Science10.1016/j.tcs.2022.10.019940:PB(123-145)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1016/j.tcs.2022.10.019
Urquhart NHart E(2023)Improving the Size and Quality of MAP-Elites Containers via Multiple Emitters and Decoders for Urban LogisticsApplications of Evolutionary Computation10.1007/978-3-031-30229-9_3(35-52)Online publication date: 12-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-30229-9_3
Bossek JNeumann ANeumann FWagner M(2022)Evolutionary diversity optimization for combinatorial optimizationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533626(824-842)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533626
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