EasyAnalyzer: An Object-Oriented Framework for the Experimental Analysis of Stochastic Local Search Algorithms

  • Luca Di Gaspero
  • Andrea Roli
  • Andrea Schaerf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4638)


One of the aspects of applying software engineering to Stochastic Local Search (SLS) is the principled analysis of the features of the problem instances and the behavior of SLS algorithms, which —because of their stochastic nature— might need sophisticated statistical tools.

In this paper we describe EasyAnalyzer, an object-oriented framework for the experimental analysis of SLS algorithms, developed in the C++ language. EasyAnalyzer integrates with EasyLocal++, a framework for the development of SLS algorithms, in order to provide a unified development and analysis environment. Moreover, the tool has been designed so that it can be easily interfaced also with SLS solvers developed using other languages/tools and/or with command-line executables.

We show an example of the use of EasyAnalyzer applied to the analysis of SLS algorithms for the k-GraphColoring problem.


Search Space Local Search Edge Density Stochastic Local Search Solver Interface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Michel, L., Van Hentenryck, P.: Localizer. Constraints 5(1–2), 43–84 (2000)zbMATHCrossRefGoogle Scholar
  2. 2.
    Van Hentenryck, P., Michel, L. (eds.): Constraint-Based Local Search. MIT Press, Cambridge (2005)Google Scholar
  3. 3.
    Van Hentenryck, P., Michel, L.: Control abstractions for local search. Constraints 10(2), 137–157 (2005)zbMATHCrossRefGoogle Scholar
  4. 4.
    Fink, A., Voß, S.: HotFrame: A heuristic optimization framework. In: [25] pp. 81–154Google Scholar
  5. 5.
    Cahon, S., Melab, N., Talbi, E.G.: ParadisEO: A framework for the reusable design of parallel and distributed metaheuristics. Journal of Heuristics 10(3), 357–380 (2004)CrossRefGoogle Scholar
  6. 6.
    Voudouris, C., Dorne, R., Lesaint, D., Liret, A.: iOpt: A software toolkit for heuristic search methods. In: Walsh, T. (ed.) CP 2001. LNCS, vol. 2239, pp. 716–719. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Di Gaspero, L., Schaerf, A.: Writing local search algorithms using EasyLocal++. In: [25]Google Scholar
  8. 8.
    Di Gaspero, L., Schaerf, A.: EasyLocal++: An object-oriented framework for flexible design of local search algorithms. Software—Practice and Experience 33(8), 733–765 (2003)CrossRefGoogle Scholar
  9. 9.
    Hoos, H., Stützle, T.: Stochastic Local Search Foundations and Applications. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  10. 10.
    Halim, S., Yap, R., Lau, H.: Viz: a visual analysis suite for explaining local search behavior. In: UIST 2006. Proceedings of the 19th annual ACM symposium on User interface software and technology, pp. 57–66. ACM Press, New York (2006)CrossRefGoogle Scholar
  11. 11.
    Lau, H., Wan, W., Lim, M., Halim, S.: A development framework for rapid meta-heuristics hybridization. In: Proceedings of the 28th Annual International Computer Software and Applications Conference (COMPSAC 2004), pp. 362–367 (2004)Google Scholar
  12. 12.
    Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns: elements of reusable object-oriented software. Addison-Wesley Publishing, Reading (1995)Google Scholar
  13. 13.
    Fonlupt, C., Robilliard, D., Preux, P., Talbi, E.G.: Fitness landscapes and performance of metaheuristic. In: Voß, S., Martello, S., Osman, I., Roucairol, C. (eds.) Metaheuristics – Advances and Trends in Local Search Paradigms for Optimization, pp. 255–266. Kluwer Academic Publishers, Dordrecht (1999)Google Scholar
  14. 14.
    Pilone, D., Pitman, N.: UML 2.0 in a Nutshell. O’Reilly Media, Inc., Sebastopol (2005)Google Scholar
  15. 15.
    Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: GECCO 2002. Proceedings of the Genetic and Evolutionary Computation Conference, New York, USA (9-13 July 2002), pp. 11–18. Morgan Kaufmann Publishers, San Francisco (2002)Google Scholar
  16. 16.
    Prestwich, S., Roli, A.: Symmetry breaking and local search spaces. In: Barták, R., Milano, M. (eds.) CPAIOR 2005. LNCS, vol. 3524, Springer, Heidelberg (2005)Google Scholar
  17. 17.
    Jones, T., Rawlins, G.: Reverse hillclimbing, genetic algorithms and the busy beaver problem. In: ICGA 1993. Genetic Algorithms: Proceedings of the Fifth International Conference, San Mateo (CA), USA, pp. 70–75. Morgan Kaufmann Publishers, San Francisco (1993)Google Scholar
  18. 18.
    Birattari, M.: The race package for R. racing methods for the selection of the best. Technical Report TR/IRIDIA/2003-37, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium (2003)Google Scholar
  19. 19.
    Taillard, E.: Few guidelines for analyzing methods. In: Proceedings of the 6th Metaheuristics International Conference (MIC 2005), Vienna, Austria (August 2005)Google Scholar
  20. 20.
    Garey, M.R., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, New York (1979)zbMATHGoogle Scholar
  21. 21.
    Hertz, A., de Werra, D.: Using tabu search techniques for graph coloring. Computing 39(4), 345–351 (1987)zbMATHCrossRefGoogle Scholar
  22. 22.
    Culberson, J.: Graph coloring page. URL Viewed: March 2007, Updated: March 2004, (2004),
  23. 23.
    Di Gaspero, L., Chiarandini, M., Schaerf, A.: A study on the short-term prohibition mechanisms in tabu search. In: Proc. of the 17th European Conf. on Artificial Intelligence (ECAI-2006) Riva del Garda, Italy pp. 83–87 (2006)Google Scholar
  24. 24.
    Chiarandini, M., Basso, D., Stützle, T.: Statistical methods for the comparison of stochastic optimizers. In: Proceedings of the 6th Metaheuristics International Conference (MIC 2005), Vienna, Austria, pp. 189–195 (2005)Google Scholar
  25. 25.
    Voß, S., Woodruff, D. (eds.): Optimization Software Class Libraries. OR/CS. Kluwer Academic Publishers, Dordrecht, the Netherlands (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Luca Di Gaspero
    • 1
  • Andrea Roli
    • 2
  • Andrea Schaerf
    • 1
  1. 1.DIEGM, University of Udine, UdineItaly
  2. 2.DEIS, University of Bologna, CesenaItaly

Personalised recommendations