Soft Computing

, Volume 14, Issue 10, pp 1117–1139 | Cite as

Evaluating a local genetic algorithm as context-independent local search operator for metaheuristics

Original Paper

Abstract

Local genetic algorithms have been designed with the aim of providing effective intensification. One of their most outstanding features is that they may help classical local search-based metaheuristics to improve their behavior. This paper focuses on experimentally investigating the role of a recent approach, the binary-coded local genetic algorithm (BLGA), as context-independent local search operator for three local search-based metaheuristics: random multi-start local search, iterated local search, and variable neighborhood search. These general-purpose models treat the objective function as a black box, allowing the search process to be context-independent. The results show that BLGA may provide an effective and efficient intensification, not only allowing these three metaheuristics to be enhanced, but also predicting successful applications in other local search-based algorithms. In addition, the empirical results reported here reveal relevant insights on the behavior of classical local search methods when they are performed as context-independent optimizers in these three well-known metaheuristics.

Keywords

Local evolutionary algorithms Local search-based metaheuristics Context-independent local search Intensification Discrete parameter optimization 

Notes

Acknowledgments

This work was supported by Research Projects TIN2008-05854 and P08-TIC-4173.

References

  1. Alcalá-Fdez J, Sánchez L, García S, del Jesus MJ, Ventura S, Garrel JM, Otero J, Romero C, Bacardit J, Rivas VM, Fernández JC, Herrera F (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318CrossRefGoogle Scholar
  2. Auger A, Hansen N (2005) Performance evaluation of an advanced local search evolutionary algorithm. In: Corne D, Michalewicz Z, McKay B, Eiben G, Fogel D, Fonseca C, Greenwood G, Raidl G, Tan KC, Zalzala A (eds) Proceedings of the IEEE international conference on evolutionary computation, vol 2. IEEE, New York , pp 1777–1784Google Scholar
  3. Beasley JE (1990) OR-library: distributing test problems by electronic mail. J Oper Res Soc 41(11):1069–1072. http://people.brunel.ac.uk/mastjjb/jeb/info.html Google Scholar
  4. Beasley JE (1998) Heuristic algorithms for the unconstrained binary quadratic programming problem. Technical report, The Management School, Imperial CollegeGoogle Scholar
  5. Blum C (2002) ACO applied to group shop scheduling: a case study on intensification and diversification. In: Dorigo M, Di Caro G, Sampels M (eds) ANTS. LNCS, vol 2463. Springer, Heidelberg, pp 14–27Google Scholar
  6. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):268–308CrossRefGoogle Scholar
  7. Boender CGE, Rinnooy-Kan AHG, Stougie L, Timmer GT (1982) A stochastic method for global optimization. Math Program 22:125–140MATHCrossRefMathSciNetGoogle Scholar
  8. Boros E, Hammer PL, Tavares G (2007) Local search heuristics for quadratic unconstrained binary optimization (QUBO). J Heuristics 13(2):99–132CrossRefGoogle Scholar
  9. Brimberg J, Mladenović N, Urošević D (2008) Local and variable neighborhood search for the k-cardinality subgraph problem. J Heuristics 14(5):501–517MATHCrossRefGoogle Scholar
  10. Campos V, Laguna M, Martí R (2005) Context-independent scatter and tabu search for permutation problems. INFORMS J Comput 17(1):111–122CrossRefMathSciNetGoogle Scholar
  11. Chelouah R, Siarry P (2003) Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions. Eur J Oper Res 148(2):335–348MATHCrossRefMathSciNetGoogle Scholar
  12. Davis L (1991) Bit-climbing, representational bias, and test suite design. In: Belew R, Booker LB (eds) Proceedings of the international conference on genetic algorithms. Morgan Kaufmann, Menlo Park, pp 18–23Google Scholar
  13. De Jong K, Potter MA, Spears WM (1997) Using problem generators to explore the effects of epistasis. In: Bäck T (ed) Proceedings of the international conference on genetic algorithms. Morgan Kaufmann, Menlo Park, pp 338–345Google Scholar
  14. Dorigo M, Stützle T (2004) Ant colony optimization. MIT, CambridgeMATHGoogle Scholar
  15. Dunham B, Fridshal D, Fridshal R, North JH (1963) Design by natural selection. Synthese 15(1):254–259CrossRefGoogle Scholar
  16. Fernandes C, Rosa A (2001) A study on non-random mating and varying population size in genetic algorithms using a royal road function. In: Proceedings of the congress on evolutionary computation. IEEE, New York, pp 60–66Google Scholar
  17. Fernandes C, Rosa AC (2008) Self-adjusting the intensity of assortative mating in genetic algorithms. Soft Comput 12(10):955–979CrossRefGoogle Scholar
  18. Fournier NG (2007) Modelling the dynamics of stochastic local search on k-sat. J Heuristics 13(6):587–639MATHCrossRefGoogle Scholar
  19. Garcia S, Molina D, Lozano M, Herrera F (2008) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: A case study on the CEC’2005 special session on real parameter optimization. J Heuristics. doi:10.1007/s10732-008-9080-4
  20. Garcia S, Fernández A, Luengo J, Herrera F (2009) A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput 13(10):959–977CrossRefGoogle Scholar
  21. García-Martínez C, Lozano M (2008) Local search based on genetic algorithms. In: Siarry P, Michalewicz Z (eds) Advances in metaheuristics for hard optimization. Natural computing. Springer, Heidelberg, pp 199–221CrossRefGoogle Scholar
  22. García-Martínez C, Lozano M, Molina D (2006) A local genetic algorithm for binary-coded problems. In: Runarsson TP, Beyer H-G, Burke E, Merelo-Guervós JJ, Whitley LD, Yao X (eds) Proceedings of the international conference on parallel problem solving from nature. LNCS, vol 4193. Springer, Heidelberg, pp 192–201Google Scholar
  23. García-Martínez C, Lozano M, Herrera F, Molina D, Sánchez AM (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res 185(3):1088–1113MATHCrossRefGoogle Scholar
  24. Glover F, Kochenberger G (eds) (2003) Handbook of metaheuristics. Kluwer, DordrechtMATHGoogle Scholar
  25. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley/Longman, Menlo Park/LondonMATHGoogle Scholar
  26. Goldberg DE, Korb B, Deb K (1989) Messy genetic algorithms: motivation, analysis, and first results. Complex Syst 3:493–530MATHMathSciNetGoogle Scholar
  27. Gortazar F, Duarte A, Laguna M, Martí R (2008) Context-independent scatter search for binary problems. Technical report, Colorado LEEDS School of Business, University of Colorado at BoulderGoogle Scholar
  28. Hansen P, Mladenović N (2002) Variable neighborhood search. In: Glover F, Kochenberger G (eds) Handbook of metaheuristics. Kluwer, Dordrecht, pp 145–184Google Scholar
  29. Harada K, Ikeda K, Kobayashi S (2006) Hybridization of genetic algorithm and local search in multiobjective function optimization: recommendation of GA then LS. In: Cattolico M (ed) Proceedings of the genetic and evolutionary computation conference. ACM, New York, pp 667–674Google Scholar
  30. Harik G (1995) Finding multimodal solutions using restricted tournament selection. In: Eshelman LJ (ed) Proceedings of the international conference on genetic algorithms. Morgan Kaufmann, Menlo Park, pp 24–31Google Scholar
  31. Helmberg C, Rendl F (2000) A spectral bundle method for semidefinite programming. SIAM J Optim 10(3):673–696MATHCrossRefMathSciNetGoogle Scholar
  32. Herrera F, Lozano M (2000) Gradual distributed real-coded genetic algorithms. IEEE Trans Evol Comput 4(1):43–63CrossRefGoogle Scholar
  33. Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann ArborGoogle Scholar
  34. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70MATHMathSciNetGoogle Scholar
  35. Hoos HH, Stützle T (2004) Stochastic local search. Morgan Kaufmann Publishers, San FranciscoGoogle Scholar
  36. Iman RL, Davenport JM (1980) Approximations of the critical region of the Friedman statistic. In: Communications in statistics. pp 571–595Google Scholar
  37. Ishibuchi H, Hitotsuyanagi Y, Tsukamoto N, Nojima Y (2009) Use of biased neighborhood structures in multiobjective memetic algorithms. Soft Comput 13(8–9):795–810CrossRefGoogle Scholar
  38. Jones T (1995) Crossover, macromutation, and population-based search. In: Eshelman L (ed) Proceedings of the sixth international conference on genetic algorithms. Morgan Kaufmann, Menlo Park, pp 73–80Google Scholar
  39. Karp RM (1972) Reducibility among combinatorial problems. In: Miller R, Thatcher J (eds) Complexity of computer computations. Plenum, NY, pp 85–103Google Scholar
  40. Katayama K, Narihisa H (2001) A variant k-opt local search heuristic for binary quadratic programming. Trans IEICE (A) J84-A(3):430–435Google Scholar
  41. Katayama K, Narihisa H (2005) An evolutionary approach for the maximum diversity problem. In: Recent advances in memetic algorithms. Springer, Heidelberg, pp 31–47Google Scholar
  42. Kauffman SA (1989) Adaptation on rugged fitness landscapes. Lec Sci Complex 1:527–618Google Scholar
  43. Kazarlis SA, Papadakis SE, Theocharis JB, Petridis V (2001) Microgenetic algorithms as generalized hill-climbing operators for GA optimization. IEEE Trans Evol Comput 5(3):204–217CrossRefGoogle Scholar
  44. Kong M, Tian P, Kao Y (2008) A new ant colony optimization algorithm for the multidimensional knapsack problem. Comput Oper Res 35(8):2672–2683MATHMathSciNetGoogle Scholar
  45. Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms: Model, taxonomy and design issues. IEEE Trans Evol Comput 9(5):474–488CrossRefGoogle Scholar
  46. Laguna M (2003) Scatter search. Kluwer, BostonMATHGoogle Scholar
  47. Lima CF, Pelikan M, Sastry K, Butz M, Goldberg DE, Lobo FG (2006) Substructural neighborhoods for local search in the bayesian optimization algorithm. In: Proceedings of the international conference on parallel problem solving from nature. LNCS, vol 4193, pp 232–241Google Scholar
  48. Lin S, Kernighan BW (1973) An effective heuristic algorithm for the traveling-salesman problem. Oper Res 21(2):498–516MATHCrossRefMathSciNetGoogle Scholar
  49. Lourenço HR, Martin O, Stützle T (2003) Iterated local search. In: Glover F, Kochenberger G (eds) Handbook of metaheuristics, Kluwer, Dordrecht, pp 321–353Google Scholar
  50. Lozano M, García-Martínez C (2010) Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: overview and progress report. Comput Oper Res 37:481–497MATHCrossRefMathSciNetGoogle Scholar
  51. Lozano M, Herrera F, Krasnogor N, Molina D (2004) Real-coded memetic algorithms with crossover hill-climbing. Evol Comput 12(3):273–302CrossRefGoogle Scholar
  52. Mahfoud SW (1992) Crowding and preselection revised. In: Männer R, Manderick B (eds) Parallel problem solving from nature, vol 2. Elsevier Science, London, pp 27–36Google Scholar
  53. Marti R (2003) Multi-start methods. In: Glover F, Kochenberger G (eds) Handbook of metaheuristics. Kluwer, Dordrech, pp 355–368Google Scholar
  54. Martí R, Moreno-Vega JM, Duarte A (2009) Advanced multi-start methods, 2nd edn. In: Handbook of metaheuristics. Springer, HeidelbergGoogle Scholar
  55. Merz P (2001) On the performance of memetic algorithms in combinatorial optimization. In: Second workshop on memetic algorithms, genetic and evolutionary computation conference. Morgan Kaufmann, Menlo Park, pp 168–173Google Scholar
  56. Merz P, Katayama K (2004) Memetic algorithms for the unconstrained binary quadratic programming problem. Biosystems 79(1–3):99–118CrossRefGoogle Scholar
  57. Moscato P (1999) Memetic algorithms: a short introduction. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimization. McGraw-Hill, NY, pp 219–234Google Scholar
  58. Mutoh A, Tanahashi F, Kato S, Itoh H (2006) Efficient real-coded genetic algorithms with flexible-step crossover. Trans Electron Inf Syst 126(5):654–660Google Scholar
  59. Nguyen HD, Yoshihara I, Yamamori K, Yasunaga M (2007) Implementation of effective hybrid GA for large-scale traveling salesman problems. IEEE Trans Syst Man Cybern B 37(1):92–99CrossRefGoogle Scholar
  60. Noman N, Iba H (2008) Accelerating differential evolution using an adaptive local search. IEEE Trans Evol Comput 12(1):107–125CrossRefGoogle Scholar
  61. O’Reilly UM, Oppacher F (1995) Hybridized crossover-based search techniques for program discovery. In: Proceedings of the world conference on evolutionary computation, vol 2, pp 573–578Google Scholar
  62. Peng G, Ichiro I, Shigeru N (2007) Application of genetic recombination to genetic local search in TSP. Int J Inf Technol 13(1):57–66Google Scholar
  63. Potts JC, Giddens TD, Yadav SB (1994) The development and evaluation of an improved genetic algorithm based on migration and artificial selection. IEEE Trans Syst Man Cybern 24:73–86CrossRefGoogle Scholar
  64. Raidl GR (2006) A unified view on hybrid metaheuristics. In: Almeida F, Aguilera MJB Blesa, Blum C, Vega JM Moreno, Pérez M Pérez, Roli A, Sampels M (eds) Hybrid metaheuristics. LNCS, vol 4030. Springer, Heidelberg, pp 1–126Google Scholar
  65. Randall M (2006) Search space reduction as a tool for achieving intensification and diversification in ant colony optimisation. In: Ali M, Dapoigny R (eds) LNCS, vol 4031. Springer, Heidelberg, pp 254–262Google Scholar
  66. Ray SS, Bandyopadhyay S, Pal SK (2007) Genetic operators for combinatorial optimization in TSP and microarray gene ordering. App Intell 26(3):183–195CrossRefGoogle Scholar
  67. Resende MGC, Ribeiro CC (2003) Greedy randomized adaptive search procedures. In: Glover F, Kochenberger G (eds) Handbook of metaheuristics. Kluwer, Dordrecht, pp 219–249Google Scholar
  68. Sastry K, Goldberg DE (2004) Designing competent mutation operators via probabilistic model building of neighborhoods. In: Deb K, Poli R, Banzhaf W, Beyer H-G, Burk EK, Darwen PJ, Dasgupta D, Floreano D, Foster JA, Harman M, Holland O, Lanzi PL, Spector L, Tettamanzi A, Thierens D, Tyrrel AM (eds) Proceedings of the conference on genetic and evolutionary computation. LNCS, vol 3103, pp 114–125Google Scholar
  69. Siarry P, Michalewicz Z (eds) (2008) Advances in metaheuristics for hard optimization. Natural Computing, SpringerGoogle Scholar
  70. Smith K, Hoos HH, Stützle T (2003) Iterated robust tabu search for MAX-SAT. In: Carbonell JG, Siekmann J (eds) Proceedings of the Canadian society for computational studies of intelligence conference. LNCS, vol 2671. Springer, Heidelberg, pp 129–144Google Scholar
  71. Soak S-M, Lee S-W, Mahalik NP, Ahn B-H (2006) A new memetic algorithm using particle swarm optimization and genetic algorithm. In: Knowledge-based intelligent information and engineering systems. LNCS, vol 4251. Springer, Berlin, pp 122–129Google Scholar
  72. Spears WM (2000) Evolutionary algorithms: the role of mutation and recombination. Springer, HeidelbergMATHGoogle Scholar
  73. Spears WM, De Jong KA (1991) On the virtues of parameterized uniform crossover. In: Belew R, Booker LB (eds) Proceedings of the international conference on genetic algorithms. Morgan Kaufmann, Menlo Park, pp 230–236Google Scholar
  74. Sywerda G (1989) Uniform crossover in genetic algorithms. In: Schaffer JD (ed) Proceedings of the international conference on genetic algorithms. Morgan Kaufmann, Menlo Park, pp 2–9Google Scholar
  75. Talbi EG (2002) A taxonomy of hybrid metaheuristics. J Heuristics 8(5):541–564CrossRefGoogle Scholar
  76. Thierens D (2004) Population-based iterated local search: restricting neighborhood search by crossover. In: Deb K, Poli R, Banzhaf W, Beyer H-G, Burk EK, Darwen PJ, Dasgupta D, Floreano D, Foster JA, Harman M, Holland O, Lanzi PL, Spector L, Tettamanzi A, Thierens D, Tyrrel AM (eds) Proceedings of the genetic and evolutionary computation conference. LNCS, vol 3103. Springer, Heidelberg, pp 234–245Google Scholar
  77. Tsai H-K, Yang J-M, Tsai Y-F, Kao C-Y (2004) An evolutionary algorithm for large traveling salesman problems. IEEE Trans Syst Man Cybern 34(4):1718–1729CrossRefGoogle Scholar
  78. Tsutsui S, Ghosh A, Corne D, Fujimoto Y (1997) A real coded genetic algorithm with an explorer and an exploiter population. In: Bäck T (ed) Proceedings of the international conference on genetic algorithms. Morgan Kaufmann, Menlo Park, pp 238–245Google Scholar
  79. Ventura S, Romero C, Zafra A, Delgado JA, Hervás-Martínez C (2008) JCLEC: A java framework for evolutionary computation. Soft Comput 12(4):381–392CrossRefGoogle Scholar
  80. Wang H, Wang D, Yang S (2009) A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Comput 13(8–9):763–780CrossRefGoogle Scholar
  81. Whitley D (1989) The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best. In: Schaffer JD (ed) Proceedings of the international conference on genetic algorithms. Morgan Kaufmann, Menlo Park, pp 116–121Google Scholar
  82. Zar JH (1999) Biostatistical analysis. Prentice Hall, Englewood CliffsGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Department of Computing and Numerical AnalysisUniversity of CórdobaCórdobaSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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