Hyper-heuristic

A hyper-heuristic is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics (or components of such heuristics) to efficiently solve computational search problems. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem.[1][2][3]

There might be multiple heuristics from which one can choose for solving a problem, and each heuristic has its own strength and weakness. The idea is to automatically devise algorithms by combining the strength and compensating for the weakness of known heuristics.[4] In a typical hyper-heuristic framework there is a high-level methodology and a set of low-level heuristics (either constructive or perturbative heuristics). Given a problem instance, the high-level method selects which low-level heuristic should be applied at any given time, depending upon the current problem state (or search stage) determined by features.[2][5][6]

  1. ^ E. K. Burke, E. Hart, G. Kendall, J. Newall, P. Ross, and S. Schulenburg, Hyper-heuristics: An emerging direction in modern search technology, Handbook of Metaheuristics (F. Glover and G. Kochenberger, eds.), Kluwer, 2003, pp. 457–474.
  2. ^ a b P. Ross, Hyper-heuristics, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques (E. K. Burke and G. Kendall, eds.), Springer, 2005, pp. 529-556.
  3. ^ E. Ozcan, B. Bilgin, E. E. Korkmaz, A Comprehensive Analysis of Hyper-heuristics[dead link], Intelligent Data Analysis, 12:1, pp. 3-23, 2008.
  4. ^ E. Ozcan, B. Bilgin, E. E. Korkmaz, Hill Climbers and Mutational Heuristics in Hyperheuristics, Lecture Notes in Computer Science, Springer-Verlag, The 9th International Conference on Parallel Problem Solving From Nature, 2006, pp. 202-211.
  5. ^ Amaya, I., Ortiz-Bayliss, J.C., Rosales-Perez, A., Gutierrez-Rodriguez, A.E., Conant-Pablos, S.E., Terashima-Marin, H. and Coello, C.A.C., 2018. Enhancing Selection Hyper-Heuristics via Feature Transformations. IEEE Computational Intelligence Magazine, 13(2), pp.30-41. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8335843
  6. ^ Amaya, I., Ortiz-Bayliss, J.C., Gutiérrez-Rodríguez, A.E., Terashima-Marín, H. and Coello, C.A.C., 2017, June. Improving hyper-heuristic performance through feature transformation. In 2017 IEEE Congress on Evolutionary Computation (CEC) (pp. 2614-2621). IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7969623