skip to main content
10.1145/3319619.3321947acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

Can route planning be smarter with transfer optimization?

Published: 13 July 2019 Publication History

Abstract

We aim to showcase the benefit of transfer optimization for route planning problems by illustrating how the solution accuracy of travelling salesman problem instances can be enhanced via autonomous and positive transfer of knowledge from related source problems that have been encountered previously. Our approach is able to achieve better solution accuracy by exploiting useful past experiences at runtime, based on a source-target similarity measure learned online.

References

[1]
Abhishek Gupta, Yew-Soon Ong, and Liang. Feng. 2018. Insights on transfer optimization: Because experience is the best teacher. IEEE Transactions on Emerging Topics in Computational Intelligence, 2, 1 (2018), 51--64
[2]
Bingshui Da, Abhishek Gupta, and Yew-Soon Ong. 2018. Curbing Negative Influences Online for Seamless Transfer Evolutionary Optimization. IEEE Transactions on Cybernetics, 99 (2018), 1--14.
[3]
Shigeyoshi Tsutsui. 2002. Probabilistic model-building genetic algorithms in permutation representation domain using edge histogram. In Proceedings of the Seventh International Conference on Parallel Problem Solving from Nature, pp. 224--233.
[4]
David E. Goldberg. 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley: Reading, MA.
[5]
Gerhard Reinelt. 1991. TSPLIB---A traveling salesman problem library. ORSA journal on computing, 3, 4 (1991), 376--384.

Cited By

View all
  • (2023)Scalable Transfer Evolutionary Optimization: Coping With Big Task InstancesIEEE Transactions on Cybernetics10.1109/TCYB.2022.316439953:10(6160-6172)Online publication date: Oct-2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. evolutionary algorithms
  2. route planning
  3. transfer optimization

Qualifiers

  • Research-article

Conference

GECCO '19
Sponsor:
GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

Acceptance Rates

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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)0
Reflects downloads up to 28 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Scalable Transfer Evolutionary Optimization: Coping With Big Task InstancesIEEE Transactions on Cybernetics10.1109/TCYB.2022.316439953:10(6160-6172)Online publication date: Oct-2023

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media