Advertisement

Hybridisation of Evolutionary Algorithms Through Hyper-heuristics for Global Continuous Optimisation

  • Eduardo SegredoEmail author
  • Eduardo Lalla-Ruiz
  • Emma Hart
  • Ben Paechter
  • Stefan Voß
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10079)

Abstract

Choosing the correct algorithm to solve a problem still remains an issue 40 years after the Algorithm Selection Problem was first posed. Here we propose a hyper-heuristic which can apply one of two meta-heuristics at the current stage of the search. A scoring function is used to select the most appropriate algorithm based on an estimate of the improvement that might be made by applying each algorithm. We use a differential evolution algorithm and a genetic algorithm as the two meta-heuristics and assess performance on a suite of 18 functions provided by the Generalization-based Contest in Global Optimization (genopt). The experimental evaluation shows that the hybridisation is able to provide an improvement with respect to the results obtained by both the differential evolution scheme and the genetic algorithm when they are executed independently. In addition, the high performance of our hybrid approach allowed two out of the three prizes available at genopt to be obtained.

Keywords

Global search Differential evolution Genetic algorithm Global continuous optimisation Hyper-heuristic 

References

  1. 1.
    Bingül, Z., Karahan, O.: A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control. Expert Syst. Appl. 38(1), 1017–1031 (2011)CrossRefGoogle Scholar
  2. 2.
    Burke, E.K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Qu, R.: Hyper-heuristics: a survey of the state of the art. J. Oper. Res. Soc. 64(12), 1695–1724 (2013)CrossRefGoogle Scholar
  3. 3.
    Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 57, pp. 457–474. Springer US, New York (2003)CrossRefGoogle Scholar
  4. 4.
    Das, S., Mullick, S.S., Suganthan, P.: Recent advances in differential evolution - an updated survey. Swarm Evol. Comput. 27, 1–30 (2016)CrossRefGoogle Scholar
  5. 5.
    Dasgupta, D., Michalewicz, Z.: Evolutionary Algorithms in Engineering Applications. Springer Science & Business Media, Berlin (2013)zbMATHGoogle Scholar
  6. 6.
    Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9, 115–148 (1995)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Di Pillo, G., Lucidi, S., Rinaldi, F.: A derivative-free algorithm for constrained global optimization based on exact penalty functions. J. Optim. Theory Appl. 164(3), 862–882 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2015)CrossRefzbMATHGoogle Scholar
  9. 9.
    Gaviano, M., Kvasov, D.E., Lera, D., Sergeyev, Y.D.: Algorithm 829: software for generation of classes of test functions with known local and global minima for global optimization. ACM Trans. Math. Softw. 29(4), 469–480 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Reading (1989)zbMATHGoogle Scholar
  11. 11.
    Guo, Z., Liu, G., Li, D., Wang, S.: Self-adaptive differential evolution with global neighborhood search. Soft Comput., 1–10 (2016, in press)Google Scholar
  12. 12.
    León, C., Miranda, G., Segura, C.: METCO: a parallel plugin-based framework for multi-objective optimization. Int. J. Artif. Intell. Tools 18(4), 569–588 (2009)CrossRefGoogle Scholar
  13. 13.
    Li, Y.L., Zhan, Z.H., Gong, Y.J., Chen, W.N., Zhang, J., Li, Y.: Differential evolution with an evolution path: a DEEP evolutionary algorithm. IEEE Trans. Cybern. 45(9), 1798–1810 (2015)CrossRefGoogle Scholar
  14. 14.
    Liu, J., Teo, K.L., Wang, X., Wu, C.: An exact penalty function-based differential search algorithm for constrained global optimization. Soft Comput. 20(4), 1305–1313 (2016)CrossRefGoogle Scholar
  15. 15.
    Rice, J.R.: The algorithm selection problem. Adv. Comput. 15, 65–118 (1976). ElsevierCrossRefGoogle Scholar
  16. 16.
    Segredo, E., Segura, C., León, C.: Memetic algorithms and hyperheuristics applied to a multiobjectivised two-dimensional packing problem. J. Glob. Optim. 58(4), 769–794 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Segredo, E., Segura, C., León, C.: Fuzzy logic-controlled diversity-based multi-objective memetic algorithm applied to a frequency assignment problem. Eng. Appl. Artif. Intell. 30, 199–212 (2014)CrossRefGoogle Scholar
  18. 18.
    Segura, C., Coello, C.A.C., Segredo, E., Aguirre, A.H.: A novel diversity-based replacement strategy for evolutionary algorithms. IEEE Trans. Cybern. 1–14 (2015, in press)Google Scholar
  19. 19.
    Segura, C., Coello Coello, C.A., Segredo, E., León, C.: On the adaptation of the mutation scale factor in differential evolution. Optim. Lett. 9(1), 189–198 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Segura, C., Segredo, E., León, C.: Analysing the robustness of multiobjectivisation approaches applied to large scale optimisation problems. In: Tantar, E., Tantar, A.-A., Bouvry, P., Del Moral, P., Legrand, P., Coello Coello, C.A., Schütze, O. (eds.) EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. SCI, vol. 447, pp. 365–391. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  21. 21.
    Storn, R.: On the usage of differential evolution for function optimization. In: 1996 Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 519–523. IEEE (1996)Google Scholar
  22. 22.
    Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Thomsen, R.: Flexible ligand docking using differential evolution. In: 2003 IEEE Congress on Evolutionary Computation (CEC), vol. 4, pp. 2354–2361. IEEE (2003)Google Scholar
  24. 24.
    Vinkó, T., Izzo, D.: Learning the best combination of solvers in a distributed global optimization environment. In: Proceedings of Advances in Global Optimization: Methods and Applications (AGO), Mykonos, Greece, pp. 13–17, June 2007Google Scholar
  25. 25.
    Yao, X.: Evolutionary Computation: Theory and Applications. World Scientific, Singapore (1999)CrossRefGoogle Scholar
  26. 26.
    Yuan, X., Zhang, Y., Wang, L., Yuan, Y.: An enhanced differential evolution algorithm for daily optimal hydro generation scheduling. Comput. Math. Appl. 55(11), 2458–2468 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Zhang, J., Sanderson, A.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)CrossRefGoogle Scholar
  28. 28.
    Zhu, H., Wang, Y., Wang, K., Chen, Y.: Particle swarm optimization (PSO) for the constrained portfolio optimization problem. Expert Syst. Appl. 38(8), 10161–10169 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Eduardo Segredo
    • 1
    Email author
  • Eduardo Lalla-Ruiz
    • 2
  • Emma Hart
    • 1
  • Ben Paechter
    • 1
  • Stefan Voß
    • 2
  1. 1.School of ComputingEdinburgh Napier UniversityEdinburghScotland, UK
  2. 2.Institute of Information SystemsUniversity of HamburgHamburgGermany

Personalised recommendations