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Fairer Comparisons for Travelling Salesman Problem Solutions Using Hash Functions

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13987))

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Abstract

Fitness functions fail to differentiate between different solutions with the same fitness, and this lack of ability to distinguish between solutions can have a detrimental effect on the search process. We investigate, for the Travelling Salesman Problem (TSP), the impact of using a hash function to differentiate solutions during the search process. Whereas this work is not intended to improve the state-of-the-art of the TSP solvers, it nevertheless reveals a positive effect when the hash function is used.

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Notes

  1. 1.

    The comparison between all the functions is available at https://elkrari.com/hashfunctions/.

References

  1. Brownlee, A.E., Woodward, J.R., Swan, J.: Metaheuristic design pattern: surrogate fitness functions. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1261–1264 (2015)

    Google Scholar 

  2. Brucker, P.: Scheduling Algorithms. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-69516-5

  3. Cai, S., Su, K., Sattar, A.: Local search with edge weighting and configuration checking heuristics for minimum vertex cover. Artif. Intell. 175(9–10), 1672–1696 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  4. Croes, G.A.: A method for solving traveling-salesman problems. Oper. Res. 6(6), 791–812 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  5. El Krari, M., Guibadj, R.N., Woodward, J., Robilliard, D.: Introducing a hash function for the travelling salesman problem for differentiating solutions. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 123–124. GECCO 2021, Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3449726.3459580

  6. Fay, M.P., Proschan, M.A.: Wilcoxon-Mann-Whitney or t-test? on assumptions for hypothesis tests and multiple interpretations of decision rules. Statist. Surv. 4, 1 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  7. Flood, M.M.: The traveling-salesman problem. Oper. Res. 4(1), 61–75 (1956)

    Article  MathSciNet  MATH  Google Scholar 

  8. Hartung, E., Hoang, H.P., Mütze, T., Williams, A.: Combinatorial generation via permutation languages. In: Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1214–1225. SIAM (2020)

    Google Scholar 

  9. Hifi, M., Michrafy, M., Sbihi, A.: A reactive local search-based algorithm for the multiple-choice multi-dimensional knapsack problem. Comput. Optim. Appl. 33(2–3), 271–285 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  10. Hoos, H.H., Stützle, T.: Stochastic local search: Foundations and applications. Elsevier (2004)

    Google Scholar 

  11. Koopmans, T.C., Beckmann, M.: Assignment problems and the location of economic activities. Econometrica 25(1), 53–76 (1957)

    Article  MathSciNet  MATH  Google Scholar 

  12. Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer Science & Business Media, Cham (2013). https://doi.org/10.1007/978-3-662-04726-2

    Book  MATH  Google Scholar 

  13. Larrañaga, P., Kuijpers, C., Murga, R., Inza, I., Dizdarevic, S.: Genetic algorithms for the travelling salesman problem: A review of representations and operators. Artif. Intell. Rev. Int. Surv. Tutor. J. 13(2), 129–170 (1999)

    Google Scholar 

  14. Loiola, E.M., de Abreu, N.M.M., Boaventura-Netto, P.O., Hahn, P., Querido, T.: A survey for the quadratic assignment problem. Eur. J. Oper. Res. 176(2), 657–690 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  15. Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Statist. 50–60 (1947)

    Google Scholar 

  16. Michalewicz, Z., Fogel, D.B.: How To Solve It: Modern Heuristics. Springer Science & Business Media, Heidelberg (2013). https://doi.org/10.1007/978-3-662-07807-5

    Book  MATH  Google Scholar 

  17. Pierskalla, W.: The tri-substitution method for the three-dimensional assignment problem. Can. Oper. Res. Soc. J. 5(2), 71 (1967)

    MATH  Google Scholar 

  18. Reinelt, G.: TSPLIB-a traveling salesman problem library. ORSA J. Comput. 3(4), 376–384 (1991)

    Article  MATH  Google Scholar 

  19. Túlio A.M., T., Thibaut, V., Tony, W.: Heuristics for vehicle routing problems Sequence or set optimization? Comput. Oper. Res. 105, 118–131 (2019)

    Google Scholar 

  20. Woodruff, D.L., Zemel, E.: Hashing vectors for Tabu search. Ann. Oper. Res. 41, 123–137 (1993)

    Article  MATH  Google Scholar 

  21. Woodward, J.R., Bai, R.: Canonical representation genetic programming. In: Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 585–592. GEC 2009, Association for Computing Machinery, New York, NY, USA (2009). https://doi.org/10.1145/1543834.1543914

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Authors and Affiliations

  1. Computational Optimisation and Learning Lab, School of Computer Science, University of Nottingham, Nottingham, UK

    Mehdi El Krari

  2. Université du Littoral Côte d’Opale, EA 4491 - LISIC, Calais, France

    Rym Nesrine Guibadj & Denis Robilliard

  3. Operational Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK

    John Woodward

Authors
  1. Mehdi El Krari
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  2. Rym Nesrine Guibadj
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  3. John Woodward
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  4. Denis Robilliard
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Corresponding author

Correspondence to Mehdi El Krari .

Editor information

Editors and Affiliations

  1. Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Leslie Pérez Cáceres

  2. Université libre de Bruxelles, Bruxelles, Belgium

    Thomas Stützle

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El Krari, M., Guibadj, R.N., Woodward, J., Robilliard, D. (2023). Fairer Comparisons for Travelling Salesman Problem Solutions Using Hash Functions. In: Pérez Cáceres, L., Stützle, T. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2023. Lecture Notes in Computer Science, vol 13987. Springer, Cham. https://doi.org/10.1007/978-3-031-30035-6_1

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  • DOI: https://doi.org/10.1007/978-3-031-30035-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30034-9

  • Online ISBN: 978-3-031-30035-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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