An Investigation of Hyper Heuristic Frameworks

  • Rashmi AmardeepEmail author
  • K. ThippeSwamy
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 33)


This article presents an emerging methodology in research and optimization called hype heuristics. The new approach will increase the extent of generality within which the optimization systems operate. Compared to heuristics (Meta) technology that works in a particular class of problems, hyper heuristics leads to general systems that manage extensive variety of issue area. Hype heuristics make an intelligent choice of the correct heuristic algorithm in a given situation. The article analyzes the absolute most recent works distributed in different fields.


Hyper-heuristic Meta-heuristic Optimization search 


  1. 1.
    Cowling, P.I., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: Selected Papers of Proceedings of the Third International Conference on International Conference on the Practice and Theory of Automated Timetabling. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001)Google Scholar
  2. 2.
    Burke, E.K., MacCarthy, B.L., Petrovic, S., Qu, R.: Knowledge discovery in a hyperheuristic for course timetabling using case based reasoning. In: Proceedings of the Fourth International Conference on the Practice and Theory of Automated Timetabling (PATAT 2002), Ghent, Belgium, August 2002Google Scholar
  3. 3.
    Petrovic, S., Qu, R.: Case-based reasoning as a heuristic selector in a hyper-heuristic for course timetabling. In: Proceedings of the Sixth International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES 2002), Crema, Italy, September 2002Google Scholar
  4. 4.
    Cross, S.E., Walker, E.: Dart: applying knowledge-based planning and scheduling to crisis action planning. In: Zweben, M., Fox, M.S. (eds.) Intelligent Scheduling. Morgan Kaufmann, San Mateo (1994)Google Scholar
  5. 5.
    Minton, S.: Learning Search Control Knowledge: An Explanation-Based Approach. Kluwer, Boston (1988)CrossRefGoogle Scholar
  6. 6.
    Gratch, J., Chein, S., de Jong, G.: Learning search control knowledge for deep space network scheduling. In: Proceedings of the Tenth International Conference on Machine Learning, pp. 135–142 (1993)CrossRefGoogle Scholar
  7. 7.
    Hart, E., Ross, P.M., Nelson, J.: Solving a real-world problem using an evolving heuristically driven schedule builder. Evol. Comput. 6(1), 61–80 (1998)CrossRefGoogle Scholar
  8. 8.
    Terashima-Marín, H., Ross, P.M., Valenzuela-Rendón, M.: Evolution of constraint satisfaction strategies in examination timetabling. In: Banzhaf, W., et al. (eds.) Proceedings of the GECCO 1999 Genetic and Evolutionary Computation Conference, pp. 635–642. Morgan Kaufmann, San Mateo (1999)Google Scholar
  9. 9.
    Montazeri, M., Baghshah, M.S., Enhesari, A.: Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of Lung Cancer Disease.
  10. 10.
    Han, L., Kendall, G.: An investigation of a tabu assisted hyper-heuristic genetic algorithm. In: IEEE 2003 Conference (2003)Google Scholar
  11. 11.
    Kendall, G., Mohamad, M.: Channel assignment in cellular communication using a great deluge hyper-heuristic. In: IEEE 2004 International Conference (2004)Google Scholar
  12. 12.
    Tsai, C.-W., Song, H.-J., Chiang, M.-C.: A hyper-heuristic clustering algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics, COEX, Seoul, Korea (2012)Google Scholar
  13. 13.
    Kabirzadeh, S., Rahbari, D., Nickray, M.: A hyper heuristic algorithm for scheduling of fog networks. In: Proceeding of the 21st Conference of Fruct AssociationGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Sri Siddhartha Academy of Higher EducationTumkurIndia
  2. 2.Visvesvaraya Technological University, PG Regional Center MysoreMysoreIndia

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