A Meta-Genetic Algorithm for Hybridizing Metaheuristics

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10423)

Abstract

The research presented in this paper forms part of the initiative aimed at automating the design of intelligent techniques to make them more accessible to non-experts. This study focuses on automating the hybridization of metaheuristics and parameter tuning of the individual metaheuristics. It is an initial attempt at testing the feasibility to automate this design process. A genetic algorithm is used for this purpose. Each hybrid metaheuristic is a combination of metaheuristics and corresponding parameter values. The genetic algorithm explores the space of these combinations. The genetic algorithm is evaluated by applying it to solve the symmetric travelling salesman problem. The evolved hybrid metaheuristics are found to perform competitively with the manually designed hybrid approaches from previous studies and outperform the metaheuristics applied individually. The study has also revealed the potential reusability of the evolved hybrids. Based on the success of this initial study, different problem domains shall be used to verify the automation approach to the design of hybrid metaheuristics.

Keywords

Metaheuristics Hybrid metaheuristics Meta-genetic algorithm 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Adriaensen, S., Brys, T., Nowé, A.: Designing reusable metaheuristic methods: a semi-automated approach. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 2969–2976. IEEE (2014)Google Scholar
  2. 2.
    Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization, vol. 45. Springer Science & Business Media, Heidelebrg (2008)MATHGoogle Scholar
  3. 3.
    Bhanu, S.M.S., Gopalan, N.: A hyper-heuristic approach for efficient resource scheduling in grid. Int. J. Comput. Commun. Control 3(3), 249–258 (2008)CrossRefGoogle Scholar
  4. 4.
    Blum, C., Puchinger, J., Raidl, G.R., Roli, A.: Hybrid metaheuristics in combinatorial optimization: a survey. Appl. Soft Comput. 11(6), 4135–4151 (2011)CrossRefMATHGoogle Scholar
  5. 5.
    Burke, E.K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Qu, R.: Hyper-heuristics: a survey of the state of the art. J. Oper. Res. Soc. 64(12), 1695–1724 (2013)CrossRefGoogle Scholar
  6. 6.
    Chen, S.M., Chien, C.Y.: Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques. Expert Syst. Appl. 38(12), 14439–14450 (2011)CrossRefGoogle Scholar
  7. 7.
    Créput, J.C., Koukam, A.: A memetic neural network for the euclidean traveling salesman problem. Neurocomputing 72(4), 1250–1264 (2009)CrossRefGoogle Scholar
  8. 8.
    Croes, G.A.: A method for solving traveling-salesman problems. Oper. Res. 6(6), 791–812 (1958)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Deng, W., Chen, R., He, B., Liu, Y., Yin, L., Guo, J.: A novel two-stage hybrid swarm intelligence optimization algorithm and application. Soft. Comput. 16(10), 1707–1722 (2012)CrossRefGoogle Scholar
  10. 10.
    Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)CrossRefGoogle Scholar
  11. 11.
    Dioşan, L., Oltean, M.: Evolutionary design of evolutionary algorithms. Genet. Program Evolvable Mach. 10(3), 263–306 (2009)CrossRefGoogle Scholar
  12. 12.
    Durstenfeld, R.: Algorithm 235: random permutation. Commun. ACM 7(7), 420 (1964)CrossRefGoogle Scholar
  13. 13.
    Eiben, Á.E., Smit, S.K.: Evolutionary algorithm parameters and methods to tune them. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds.) Autonomous Search, pp. 15–36. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21434-9_2 CrossRefGoogle Scholar
  14. 14.
    Gendreau, M., Potvin, J.: Handbook of Metaheuristics. International Series in Operations Research & Management Science. Springer, Heidelberg (2010)CrossRefMATHGoogle Scholar
  15. 15.
    Gomes, C.P., Selman, B.: Algorithm portfolios. Artif. Intell. 126(1), 43–62 (2001)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Grobler, J., Engelbrecht, A.P., Kendall, G., Yadavalli, V.: Alternative hyper-heuristic strategies for multi-method global optimization. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2010)Google Scholar
  17. 17.
    Helsgaun, K.: An effective implementation of the lin-kernighan traveling salesman heuristic. Eur. J. Oper. Res. 126(1), 106–130 (2000)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Hutter, F., Hoos, H.H., Stützle, T.: Automatic algorithm configuration based on local search. AAAI 7, 1152–1157 (2007)Google Scholar
  19. 19.
    Kanda, J., de Carvalho, A., Hruschka, E., Soares, C., Brazdil, P.: Meta-learning to select the best meta-heuristic for the traveling salesman problem: a comparison of meta-features. Neurocomputing 205, 393–406 (2016)CrossRefGoogle Scholar
  20. 20.
    Lin, Y., Bian, Z., Liu, X.: Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing-tabu search algorithm to solve the symmetrical traveling salesman problem. Appl. Soft Comput. 49, 937–952 (2016)CrossRefGoogle Scholar
  21. 21.
    Maashi, M., Özcan, E., Kendall, G.: A multi-objective hyper-heuristic based on choice function. Expert Syst. Appl. 41(9), 4475–4493 (2014)CrossRefGoogle Scholar
  22. 22.
    Mühlenbein, H., Gorges-Schleuter, M., Krämer, O.: Evolution algorithms in combinatorial optimization. Parallel Comput. 7(1), 65–85 (1988)CrossRefMATHGoogle Scholar
  23. 23.
    Osaba, E., Yang, X.S., Diaz, F., Lopez-Garcia, P., Carballedo, R.: An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Eng. Appl. Artif. Intell. 48, 59–71 (2016)CrossRefGoogle Scholar
  24. 24.
    Pillay, N.: Intelligent system design using hyper-heuristics. S. Afr. Comput. J. 56(1), 107–119 (2015)Google Scholar
  25. 25.
    Rice, J.R.: The algorithm selection problem. Adv. Comput. 15, 65–118 (1976)CrossRefGoogle Scholar
  26. 26.
    Saenphon, T., Phimoltares, S., Lursinsap, C.: Combining new fast opposite gradient search with ant colony optimization for solving travelling salesman problem. Eng. Appl. Artif. Intell. 35, 324–334 (2014)CrossRefGoogle Scholar
  27. 27.
    Talbi, E.G.: A taxonomy of hybrid metaheuristics. J. Heuristics 8(5), 541–564 (2002)CrossRefGoogle Scholar
  28. 28.
    Wu, C., Liang, Y., Lee, H.P., Lu, C.: Generalized chromosome genetic algorithm for generalized traveling salesman problems and its applications for machining. Phys. Rev. E 70(1), 016701 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of KwaZulu-NatalPietermaritzburgSouth Africa

Personalised recommendations