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Predicting Genetic Algorithm Performance on the Vehicle Routing Problem Using Information Theoretic Landscape Measures

  • Conference paper
Evolutionary Computation in Combinatorial Optimization (EvoCOP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7832))

Abstract

In this paper we examine the predictability of genetic algorithm (GA) performance using information-theoretic fitness landscape measures. The outcome of a GA is largely based on the choice of search operator, problem representation and tunable parameters (crossover and mutation rates, etc). In particular, given a problem representation the choice of search operator will determine, along with the fitness function, the structure of the landscape that the GA will search upon. Statistical and information theoretic measures have been proposed that aim to quantify properties (ruggedness, smoothness, etc) of this landscape. In this paper we concentrate on the utility of information theoretic measures to predict algorithm output for various instances of the capacitated and time-windowed vehicle routing problem. Using a clustering-based approach we identify similar landscape structures within these problems and propose to compare GA results to these clusters using performance profiles. These results highlight the potential for predicting GA performance, and providing insight self-configurable search operator design.

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References

  1. Alander, J.T., Zinchenko, L.A., Sorokin, S.N.: Analysis of fitness landscape properties for evolutionary antenna design. In: IEEE International Conference on Artificial Intelligence Systems, pp. 363–368 (2002)

    Google Scholar 

  2. Barnett, L.: Netcrawling-Optimal Evolutionary Search with Neutral Networks. In: Congress on Evolutionary Computation, pp. 30–37 (2001)

    Google Scholar 

  3. Braysy, O., Gendreau, M.: Vehicle routing problem with time windows, part ii: Metaheuristics. Transportation Science 39, 119–139 (2005)

    Article  Google Scholar 

  4. Caramia, M., Onori, R.: Experimenting crossover operators to solve the vehicle routing problem with time windows by genetic algorithms. International Journal of Operational Research 3(5), 497–514 (2008)

    Article  MATH  Google Scholar 

  5. Czech, Z.J.: Statistical measures of a fitness landscape for the vehicle routing problem. In: IEEE International Symposium on Parallel and Distributed Processing, pp. 1–8 (2008)

    Google Scholar 

  6. Czech, Z.J.: A parallel simulated annealing algorithm as a tool for fitness landscape exploration. In: Ros, A. (ed.) Parallel and Distributed Processing, pp. 247–271. In-Tech (2010)

    Google Scholar 

  7. Garey, M.R., Johnson, D.S.: Computers and Intractability, A Guide to the Theory of NP-Completeness. W. H. Freeman and Company (1979)

    Google Scholar 

  8. Jones, T.: Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, University of New Mexico (1995)

    Google Scholar 

  9. Kubiak, M.: Distance measures and fitness-distance analysis for the capacitated vehicle routing problem. In: Doerner, K.F., Gendreau, M., Greistorfer, P., Gutjahr, W., Hartl, R.F., Reimann, M. (eds.) Metaheuristics. Operations Research Computer Science Interfaces, vol. 39, pp. 345–364. Springer (2007)

    Google Scholar 

  10. Laporte, G.: Fifty years of vehicle routing. Transportation Science 43, 408–416 (2009)

    Article  MathSciNet  Google Scholar 

  11. Mattfeld, D.C., Bierwirth, C., Kopfer, H.: A search space analysis of the job shop scheduling problem. Annals of Operations Research 86, 441–453 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  12. Merz, P., Freisleben, B.: Memetic Algorithms and the Fitness Landscape of the Graph Bi-Partitioning Problem. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 765–774. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  13. Merz, P., Freisleben, B.: Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Transactions on Evolutionary Computation 4(4), 337–352 (2000)

    Article  Google Scholar 

  14. Naudts, B., Kallel, L.: A Comparison of Predictive Measures of Problem Difficulty in Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 4(1), 1–16 (2000)

    Article  Google Scholar 

  15. Nazif, H., Lee, L.S.: Optimized crossover genetic algorithm for vehicle routing problem with time windows. American Journal of Applied Sciences 7(1), 95–101 (2010)

    Article  Google Scholar 

  16. Ombuki-Berman, B., Ventresca, M.: Search difficulty of two-connected ring-based topological network designs. In: IEEE Symposium on Foundations of Computational Intelligence, pp. 267–274 (2007)

    Google Scholar 

  17. Potvin, J.: State-of-the art review evolutionary algorithms for vehicle routing. INFORMS Journal on Computing 21, 518–548 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  18. Reeves, C.: Direct statistical estimation of GA landscape properties. In: Foundations of Genetic Algorithms 6, pp. 91–107 (2000)

    Google Scholar 

  19. Runka, A., Ombuki-Berman, B., Ventresca, M.: A search space analysis for the waste collection vehicle routing problem with time windows. In: Genetic and Evolutionary Computation Conference, pp. 1813–1814 (2009)

    Google Scholar 

  20. Schiavinotto, T., Stutzle, T.: A review of metrics on permutations for search landscape analysis. Computers and Operations Research 34(10), 3143–3153 (2007)

    Article  MATH  Google Scholar 

  21. Tavares, J., Pereira, B., Costa, E.: Multidimensional knapsack problem: A fitness landscape analysis. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cynernetics 38(3), 604–616 (2008)

    Article  Google Scholar 

  22. Toth, P., Vigo, D.: The Vehicle Routing Problem. SIAM Monographs on Discrete Mathematics and Applications (2002)

    Google Scholar 

  23. Vassilev, V.K., Fogarty, T.C., Miller, J.F.: Information Characteristics and the Structure of Landscapes. Evolutionary Computation 8(1), 31–60 (2000)

    Article  Google Scholar 

  24. Vassilev, V.K., Fogarty, T.C., Miller, J.F.: Fitness Landscapes: from Theory to Application. In: Advances in Evolutionary Computation: Theory and Applications, pp. 3–44. Springer (2003)

    Google Scholar 

  25. Ventresca, M., Ombuki-Berman, B.: Search space analysis of recurrent spiking and continuous-time neural networks. In: IEEE International Joint Conference on Neural Networks, pp. 8947–8954 (2006)

    Google Scholar 

  26. Weinberger, E.: Correlated and Uncorrelated Landscapes and How to Tell the Difference. Biological Cybernetics 63, 325–336 (1990)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada

    Mario Ventresca

  2. Department of Computer Science, Brock University, St. Catharines, Canada

    Beatrice Ombuki-Berman

  3. Department of Computer Science, Carleton University, Ottawa, Canada

    Andrew Runka

Authors
  1. Mario Ventresca
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  2. Beatrice Ombuki-Berman
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  3. Andrew Runka
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Leipzig, Johannisgasse 26, 04103, Leipzig, Germany

    Martin Middendorf

  2. IKERBASQUE, Basque Foundation for Science, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel Lardizabal 1, 20018, Donostia, Spain

    Christian Blum

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Ventresca, M., Ombuki-Berman, B., Runka, A. (2013). Predicting Genetic Algorithm Performance on the Vehicle Routing Problem Using Information Theoretic Landscape Measures. In: Middendorf, M., Blum, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2013. Lecture Notes in Computer Science, vol 7832. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37198-1_19

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  • DOI: https://doi.org/10.1007/978-3-642-37198-1_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37197-4

  • Online ISBN: 978-3-642-37198-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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