skip to main content
10.1145/1569901.1570026acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

Tunneling between optima: partition crossover for the traveling salesman problem

Published:08 July 2009Publication History

ABSTRACT

A new recombination operator is introduced for the Traveling Salesman Problem called partition crossover. Theoretical and empirical results indicate that when two local optima are recombined using partition crossover, two offspring are produced that are highly likely to also be local optima. Thus, the operator is capable of jumping or tunneling from two local optima to two new and distinct local optima without searching intermediate solutions. The operator is respectful and it transmits alleles which means that 1) all common edges from the two parents are inherited and 2) the offspring are constructed using only edges inherited from the two parents. Partition crossover is not always feasible: sometimes two new Hamiltonian circuits cannot be constructed by the operator using only edges inherited from the two parents. But empirical results indicate that partition crossover is feasible 95 percent of the time when recombining randomly selected local optima. Furthermore, from a sample of local optima that are within a short random walk of the global optimum, partition crossover typically relocates the global optimum in a single move when crossover is feasible.

References

  1. K. Boese. Cost versus distance in the traveling salesman problem. Technical report, Computer Science Department, UCLA 1995.Google ScholarGoogle Scholar
  2. T. Cormen, C. Leiserson, and R. Rivest. Introduction to Algorithms. McGraw Hill, New York, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Michael R. Garey and David S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, 1979. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. David Goldberg and Jr. Robert Lingle. Alleles, Loci, and the Traveling Salesman Problem. In International Conf. on GAs, pages 154--159, 1985. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Grefensette. Incorporating Problem Specific Knowledge in Genetic Algorithms. In L. Davis, ed, Genetic Algorithms and Simulated Annealing, pages 42--60. Morgan Kaufmann, 1987.Google ScholarGoogle Scholar
  6. D. S. Johnson and L. A. McGeoch. The Traveling Salesman Problem: A Case Study in Local Optimization. In E. H. L. Aarts and J.K. Lenstra, eds, Local Search in Combinatorial Optimization, pages 215--310. Wiley, 1997.Google ScholarGoogle Scholar
  7. Keith E. Mathias and L. Darrell Whitley. Genetic Operators, the Fitness Landscape and the Traveling Salesman Problem. In Parallel Problem Solving from Nature, 2, pages 219--228. Elsevier, 1992.Google ScholarGoogle Scholar
  8. H. Mühlenbein. Evolution in Time and Space: The Parallel Genetic Algorithm. In G. Rawlins, ed, FOGA -1, pages 316--337. Morgan Kaufmann, 1991.Google ScholarGoogle Scholar
  9. Yuichi Nagata and Shigenobu Kobayashi. Edge Assembly Crossover: A High--Power Genetic Algorithm for the Traveling Salesman Problem. In T. Bäck, editor, 7th International Conf. on GAs, pages 450--457. Morgan Kaufmann, 1997.Google ScholarGoogle Scholar
  10. I. Oliver, D. Smith, and J. Holland. A Study of Permutation Crossover Operators on the Traveling Salesman Problem. In GAs and Their Applications: ICGA 2 Erlbaum, 1987. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. W. Padberg and G. Rinaldi. Optimization of a 532 City Symmetric TSP. Optimization Research Letters, 6(1):1--7, 1987.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Nicholas J. Radcliffe. The Algebra of Genetic Algorithms. Annals of Maths and Artificial Intelligence, 10:339--384, 1994.Google ScholarGoogle ScholarCross RefCross Ref
  13. N. J. Radcliffe and P. D. Surry. Fitness Variance of Formae and Performance Predictions. In D. Whitley and M. Vose, eds, FOGA -- 3, pages 51--72. Morgan Kaufmann, 1995.Google ScholarGoogle Scholar
  14. L. Darrell Whitley, Timothy Starkweather, and Daniel Shaner. The Traveling Salesman and Sequence Scheduling: Quality Solutions Using Genetic Edge Recombination. In L. Davis, ed, Handbook of Genetic Algorithms, pages 350--372. Van Nostrand Reinhold, NY, 1991.Google ScholarGoogle Scholar

Index Terms

  1. Tunneling between optima: partition crossover for the traveling salesman problem

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
      July 2009
      2036 pages
      ISBN:9781605583259
      DOI:10.1145/1569901

      Copyright © 2009 ACM

      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]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 8 July 2009

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate1,669of4,410submissions,38%

      Upcoming Conference

      GECCO '24
      Genetic and Evolutionary Computation Conference
      July 14 - 18, 2024
      Melbourne , VIC , Australia

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader