Skip to main content

Abstract

Recent works in experimental analysis of algorithms have identified the need to explain the observed performance. To understand the behavior of an algorithm it is necessary to characterize and study the factors that affect it. This work provides a summary of the main works related to the characterization of heuristic algorithms, by comparing the works done in understanding how and why algorithms follow certain behavior. The main objective of this research is to promote the improvement of the existing characterization methods and contribute to the development of methodologies for robust analysis of heuristic algorithms performance. In particular, this work studies the characterization of the optimization process of the Bin Packing Problem, exploring existing results from the literature, showing the need for further performance analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wolpert, D., Macready, W.: No free lunch theorems for optimizations. IEEE Trans. Evol. Comput. 1, 67–82 (1997)

    Article  Google Scholar 

  2. Smith-Miles, K., Lopes, L.: Measuring instance difficulty for combinatorial optimization problems. Comput. Oper. Res. 39(5), 875–889 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  3. Smyth, K.: Understanding stochastic local search algorithms: an empirical analysis of the relationship between search space structure and algorithm behaviour. The University of British Columbia, Ms. thesis (2004)

    Google Scholar 

  4. Garey, M., Jonson, D.: Computers and intractability: A guide to the theory of NP-completeness. W. H. Freeman and Company, a classic introduction to the field (1979)

    Google Scholar 

  5. Smith-Miles, K., James, R., Giffin, J., Tu, Y.: Understanding the relationship between scheduling problem structure and heuristic performance using knowledge discovery. In: Learning and Intelligent Optimization, LION 3 (2009)

    Google Scholar 

  6. Leyton-Brown, K., Nudelman, E., Shoham, Y.: Learning the empirical hardness of optimization problems: The case of combinatorial auctions. Principles Pract. Constraint Program. 2470, 556–572 (2002)

    Google Scholar 

  7. Nudelman, E., Devkar, A., Shoham, Y., Leyton-Brown, K.: Understanding random SAT: Beyond the clauses-to-variables ratio. Principles Pract. Constraint Program. 3258, 438–452 (2004)

    Google Scholar 

  8. Gagliolo, M., Schmidhuber, J.: Learning dynamic algorithm portfolios. Spec. Issue Ann. Math. Artif. Intell. 47(3–4), 295–328 (2007)

    Article  MathSciNet  Google Scholar 

  9. Madani, O., Raghavan, H., Jones, R.: On the empirical complexity of text classification problems. SRI AI Center Technical Report (2009)

    Google Scholar 

  10. Messelis, T., Haspeslagh, S., Bilgin, B., De Causmaecker, P., Vanden, G.: Towards prediction of algorithm performance in real world optimization problems. In: Proceedings of the 21st Benelux Conference on Artificial Intelligence, pp. 177–183. BNAIC, Eindhoven (2009)

    Google Scholar 

  11. Hutter, F., Xu, L., Hoos, H.H., Leyton-Brown, K.: Algorithm runtime prediction: Methods and evaluation. Artif. Intell. 206, 79–111 (2014)

    Article  MathSciNet  Google Scholar 

  12. McKay, R., Abbass, H.: Anti-correlation measures in genetic programming. In: Proceedings of the Australasia-Japan Workshop on Intelligent and Evolutionary Systems, pp. 45–51 (2001)

    Google Scholar 

  13. Burke, R., Gustafson, S., Kendall, G.: A survey and analysis of diversity measures in genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 716–723 (2002)

    Google Scholar 

  14. Thierens, D.: Predictive measures for problem representation and genetic operator design. Technical Report UU-CS-2002-055, Utrecht University: Information and Computing Sciences (2002)

    Google Scholar 

  15. Hutter, F., Hamadi, Y., Hoos, H.H., Leyton-Brown, K.: Performance prediction and automated tuning of randomized and parametric algorithms. In: Principles and Practice of Constraint Programming-CP, pp. 213–228 (2006)

    Google Scholar 

  16. Halim, S., Yap, R., Lau, H.: An integrated white + black box approach for designing and tuning stochastic local search. In: Principles and Practice of Constraint Programming–CP, pp. 332–347 (2007)

    Google Scholar 

  17. Birattari, M.: Tuning Metaheuristics: A machine learning perspective. SCI 197. Springer, Berlin (2009)

    Google Scholar 

  18. Akbaripour, H., Masehian, E.: Efficient and robust parameter tuning for heuristic algorithms. Int. J. Ind. Eng. 24(2), 143–150 (2013)

    Google Scholar 

  19. Wright, S.: The roles of mutation, inbreeding, crossbreeding and selection in evolution. In: Proceedings of the Sixth International Genetics, vol. 1, pp. 356–366 (1932)

    Google Scholar 

  20. Jones, T.: Evolutionary algorithms, fitness landscapes and search. Ph.D. thesis, The University of New Mexico (1995)

    Google Scholar 

  21. Corne, D., Oates, M., Kell, D.: Landscape state machines: tools for evolutionary algorithm performance analyses and landscape/algorithm mapping. In: Applications of Evolutionary Computing, pp. 187–198 (2003)

    Google Scholar 

  22. Mitchell, B., Mancoridis, S.: Modeling the search landscape of metaheuristic software clustering algorithms. Lecture notes in computer science. In: Proceedings of the 2003 International Conference on Genetic and Evolutionary Computation, vol. 2, pp. 2499–2510 (2003)

    Google Scholar 

  23. Merz, P.: Advanced fitness landscape analysis and the performance of memetic algorithms. Evol. Comput. Spec. Issue Magn. Algorithms 12(3), 303–325 (2004)

    MathSciNet  Google Scholar 

  24. Ochoa, G., Qu, R., Burke, E.: Analyzing the landscape of a graph based hyper-heuristic for timetabling problems. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 341–348 (2009)

    Google Scholar 

  25. Czogalla, J., Fink, A.: Fitness landscape analysis for the resource constrained project scheduling problem. In: Lecture Notes in Computer Science, Learning and Intelligent Optimization, vol. 5851, pp. 104–118 (2009)

    Google Scholar 

  26. Verel, S., Liefooghe, A., Jourdan, L., Dhaenens, C.: On the structure of multiobjective combinatorial search space: MNK-landscapes with correlated objectives. Eur. J. Oper. Res. (2012)

    Google Scholar 

  27. Merz, P., Freisleben, B.: Fitness landscapes, memetic algorithms, and greedy operators for graph bipartitioning. Evol. Comput. 8(1), 61–91 (2000)

    Article  Google Scholar 

  28. Merz, P., Freisleben, B.: Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Trans. Evol. Comput. 4(4), 337–352 (2000)

    Article  Google Scholar 

  29. Borenstein, Y.: Information landscapes. In: Genetic and Evolutionary Computation Conference, pp. 1515–1522 (2005)

    Google Scholar 

  30. Borgs, C., Chayes, J., Pittel, B.: Phase transition and finite-size scaling for the integer partitioning problem. Random Struct. Algorithms 19, 247–288 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  31. Béjar, R., Vetsikas, I., Gomes, C., Kautz, H., Selman, B.: Structure and phase transition phenomena in the VTC problem. In TASK PI Meeting Workshop (2001)

    Google Scholar 

  32. Caramanis, C.: Survey propagation iterative solutions to constraint satisfaction problems. In: Expository Writing (2003)

    Google Scholar 

  33. Achlioptas, D., Naor, A., Peres, Y.: Rigorous location of phase transitions in hard optimization problems. Nature 435(7043), 759–764 (2005)

    Article  Google Scholar 

  34. Mertens, S.: The easiest hard problem: Number partitioning. Comput. Complex. Stat. Phys. 125(2), 125–140 (2006)

    MathSciNet  Google Scholar 

  35. Piñol, C.: CSP Problems as algorithmic benchmarks: Measures, methods and models. In: Universitat de Lleida, Departament d’Informàtica i Enginyeria Industrial (2008)

    Google Scholar 

  36. Rangel-Valdez, N., Torres-Jimenez, J.: Phase transition in the bandwidth minimization problem. In: Lecture Notes in Computer Science 5845, MICAI 2009: Advances in Artificial Intelligence, pp. 372–383 (2009)

    Google Scholar 

  37. Dewenter, T., Hartmann, A.: Phase transition for cutting-plane approach to vertex-cover problem. Phys. Rev. E 86(4), 041128 (2012)

    Article  Google Scholar 

  38. Slaney, J., Walsh, T.: Backbones in optimization and approximation. In: Proceedings the 17th International Joint Conference on Artificial Intelligence (IJCAI-01), pp. 254–259 (2001)

    Google Scholar 

  39. Monasson, R., Zecchina, R., Kirkpatrick, S., Selman, B., Troyansky, L.: Determining computational complexity from characteristic ‘phase transitions’. Nature 400(6740), 133–137 (1999)

    Article  MathSciNet  Google Scholar 

  40. Zeng, G., Lu, Y.: Survey on computational complexity with phase transitions and extremal optimization. In: Proceedings of 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference Shanghai, People’s Republic of China, 16–18 Dec 2009

    Google Scholar 

  41. Singer, J., Gent, I.P., Smaill, A.: Backbone fragility and the local search cost peak. J. Artif. Intell. Res. (JAIR) 12, 235–270 (2000)

    MATH  MathSciNet  Google Scholar 

  42. Watson, J., Beck, J., Howe, A., Whitley, L.: Problem difficulty for tabu search in job-shop scheduling. Artif. Intell. 143, 189–217 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  43. Schneider, J.: Searching for backbones—a high-performance parallel algorithm for solving combinatorial optimization problems. Future Gener. Comput. Syst. 19(1), 121–131 (2003)

    Article  MATH  Google Scholar 

  44. Zhang, W.: Configuration landscape analysis and backbone guided local search: Part I: Satisfiability and maximum satisfiability. Artif. Intell. 158(1), 1–26 (2004)

    Article  MATH  Google Scholar 

  45. Zhang, W., Looks, M.: A novel local search algorithm for the travelling salesman problem that exploits backbones. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, pp. 343–350 (2005)

    Google Scholar 

  46. Zeng, G., Lu, Y., Dai, Y., Wu, Z., Mao, W., Zhang, Z., Zheng, C.: Backbone guided extremal optimization for the hard maximum satisfiability problem. Int. J. Innovative Comput. Inf. Control 8(12), 8355–8366 (2012)

    Google Scholar 

  47. Quiroz, M., Cruz-Reyes, L., Torres-Jiménez, J., Melin, P.: Improving the performance of heuristic algorithms based on exploratory data analysis. In: Recent Advances on Hybrid Intelligent Systems, pp. 361–375 (2013)

    Google Scholar 

  48. Falkenauer, E.: A hybrid grouping genetic algorithm for bin packing. J. Heuristics 2, 5–30 (1996)

    Article  Google Scholar 

  49. Alvim, A., Glover, F., Ribeiro, C., Aloise, D.: A hybrid improvement heuristic for the one-dimensional bin packing problem. J. Heuristics 10, 205–229 (2004)

    Article  Google Scholar 

  50. Fleszar, K., Charalambous, C.: Average-weight-controlled bin-oriented heuristics for the one-dimensional bin-packing problem. Eur. J. Oper. Res. 210(2), 176–184 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  51. Cruz-Reyes, L., Quiroz, M., Alvim, A., Fraire H., Gómez, C., Torres-Jiménez, J.: Heurísticas de agrupación híbridas eficientes para el problema. Computación y Sistemas 16(3) (2012)

    Google Scholar 

  52. Beasley, J.: OR-library: Distributing test problems by electronic mail. J. Oper. Res. Soc. 41(11), 1069–1072 (1990). http://people.brunel.ac.uk/~mastjjb/jeb/orlib/binpackinfo.html

    Google Scholar 

  53. Scholl, A., Klein, R.: Bin packing benchmark data sets (2014). http://www.wiwi.uni-jena.de/Entscheidung/binpp/. Accessed 24 Feb 2014

  54. ESICUP.: Euro especial interest group on cutting and packing, one dimensional cutting and packing data sets (2014). http://paginas.fe.up.pt/~esicup/tiki-list_file_gallery.php?galleryId=1. Accessed 24 Feb 2014

  55. CaPaD.: Cutting and packing at dresden university, benchmark data sets (2014). http://www.math.tu-dresden.de/~capad/cpd-ti.html#pmp. Accessed 24 Feb 2014

  56. Scholl, A., Klein, R., Jürgens, C.: Bison: A fast hybrid procedure for exactly solving the one-dimensional bin packing problem. Comput. Oper. Res. 24(7), 627–645 (1997)

    Article  MATH  Google Scholar 

  57. Wäscher, G., Gau, T.: Heuristics for the one-dimensional cutting stock problem: A computational study. OR Spektrum 18(3), 131–144 (1996)

    Article  MATH  Google Scholar 

  58. Schwerin, P., Wäscher, G.: The bin-packing problem: A problem generator and some numerical experiments with FFD packing and MTP. Int. Trans. Oper. Res. 4(5–6), 337–389 (1997)

    Google Scholar 

  59. Schoenfield, J.E.: Fast exact solution of open bin packing problems without linear programming. In: Draft, US Army Space and Missile Defense Command, Huntsville, Alabama, USA (2002)

    Google Scholar 

  60. Pérez, J., Pazos, R.A., Frausto, J., Rodríguez, G., Romero, D., Cruz, L.: A statistical approach for algorithm selection. In: Ribeiro C.C., Matins, S.L. (eds.) WEA 2004. LNCS, vol. 3059, pp. 417–431 (2004)

    Google Scholar 

  61. Cruz-Reyes, L., Gómez-Santillán, C., Schaeffer, S.E., Quiroz-Castellanos, M., Alvarez-Hernández, V.M., Pérez-Rosas, V.: Enhancing accuracy of hybrid packing systems through general-purpose characterization. In: Hybrid Artificial Intelligent Systems, pp. 26–33 (2011)

    Google Scholar 

  62. Loh, K., Golden, B., Wasil, E.: Solving the one-dimensional bin packing problem with a weight annealing heuristic. Comput. Oper. Res. 35(7), 2283–2291 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  63. Martello, S., Toth, P.: Knapsack problems: Algorithms and computer implementations. Wiley, New York (1990)

    MATH  Google Scholar 

  64. Johnson, D.S.: Fast algorithms for bin packing. J. Comput. Syst. Sci. 8(3), 272–314 (1974)

    Article  MATH  Google Scholar 

  65. Chiarandini, M., Paquete, L., Preuss, M., Ridge, E.: Experiments on metaheuristics: Methodological overview and open issues. In: Technical Report DMF-2007-03-003, The Danish Mathematical Society (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Laura Cruz-Reyes .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Quiroz, M., Cruz-Reyes, L., Torres-Jimenez, J., Santillán, C.G., Huacuja, H.J.F., Melin, P. (2014). Characterization of the Optimization Process. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-05170-3_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05169-7

  • Online ISBN: 978-3-319-05170-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics