A Software Interface for Supporting the Application of Data Science to Optimisation

  • Andrew J. ParkesEmail author
  • Ender Özcan
  • Daniel Karapetyan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8994)


Many real world problems can be solved effectively by metaheuristics in combination with neighbourhood search. However, implementing neighbourhood search for a particular problem domain can be time consuming and so it is important to get the most value from it. Hyper-heuristics aim to get such value by using a specific API such as ‘HyFlex’ to cleanly separate the search control structure from the details of the domain. Here, we discuss various longer-term additions to the HyFlex interface that will allow much richer information exchange, and so enhance learning via data science techniques, but without losing domain independence of the search control.


Combinatorial optimisation Metaheuristics Data science Machine learning 


  1. 1.
    Asta, S., Özcan, E., Parkes, A.J.: Batched mode hyper-heuristics. In: Nicosia, G., Pardalos, P. (eds.) LION 7. LNCS, vol. 7997, pp. 404–409. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Maashi, M., Özcan, E., Kendall, G.: A multi-objective hyper-heuristic based on choice function. Expert Syst. Appl. 41(9), 4475–4493 (2014)Google Scholar
  4. 4.
    Ochoa, G., Hyde, M., Curtois, T., Vazquez-Rodriguez, J.A., Walker, J., Gendreau, M., Kendall, G., McCollum, B., Parkes, A.J., Petrovic, S., Burke, E.K.: HyFlex: a benchmark framework for cross-domain heuristic search. In: Hao, J.-K., Middendorf, M. (eds.) EvoCOP 2012. LNCS, vol. 7245, pp. 136–147. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  5. 5.
    Ochoa, G., Walker, J., Hyde, M., Curtois, T.: Adaptive evolutionary algorithms and extensions to the hyflex hyper-heuristic framework. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part II. LNCS, vol. 7492, pp. 418–427. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  6. 6.
    Parkes, A.J.: Combined blackbox and algebraic architecture (CBRA). In: Proceedings of the 8th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2010), pp. 535–538 (2010)Google Scholar
  7. 7.
    Ryser-Welch, P., Miller, J.F.: A review of hyper-heuristic frameworks. In: Proceedings of the Evo20 Workshop, AISB 2014 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andrew J. Parkes
    • 1
    Email author
  • Ender Özcan
    • 1
  • Daniel Karapetyan
    • 1
  1. 1.ASAP Research Group School of Computer ScienceUniversity of NottinghamNottinghamUK

Personalised recommendations