Remember Me
Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:

OpenAIRE is about to release its new face with lots of new content and services.
During September, you may notice downtime in services, while some functionalities (e.g. user registration, validation, claiming) will be temporarily disabled.
We apologize for the inconvenience, please stay tuned!
For further information please contact helpdesk[at]openaire.eu

fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Burke, Edmund; Gendreau, Michel; Hyde, Matthew; Kendall, Graham; Ocha, Gabriela; Özcan, Ender; Qu, Rong (2013)
Publisher: Palgrave Macmillan
Journal: Journal of the Operational Research Society
Languages: English
Types: Article
Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Adenso-Diaz B and Laguna M (2006). Fine-tuning of algorithms using fractional experimental designs and local search. Operations Research 54(1): 99-114.
    • Ahmadi S, Barrone P, Cheng P, Burke EK, Cowling P and McCollum B (2003). Perturbation based variable neighbourhood search in heuristic space for examination timetabling problem. In: Kendall G, Burke EK, Petrovic S and Gendreau M (eds). Multidisciplinary International Scheduling: Theory and Applications. MISTA, Springer: New York, pp 155-171.
    • Allen S, Burke EK, Hyde MR and Kendall G (2009). Evolving reusable 3d packing heuristics with genetic programming. In: Conference on Machine Learning. Morgan Kaufmann: Amherst, MA, pp 135-142.
    • Grefenstette J (1986). Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics SMC 16(1): 122-128.
    • Grobler J, Engelbrecht A, Kendall G and Yadavalli V (2012). Investigating the use of local search for improving meta-hyperheuristic performance. In: Evolutionary Computation (CEC). IEEE: Brisbane, Australia, pp 1-8.
    • Han L and Kendall G (2003). Guided operators for a hyper - heuristic genetic algorithm. In: Gedeon TD and Fung LCC (eds). The 16th Australian Conference on Artificial Intelligence (AI 03). Springer: Perth, Australia, pp 807-820.
    • Hart E and Ross P (1998). A heuristic combination method for solving job-shop scheduling problems. In: Eiben AE, Ba¨ck T, Schoenauer M and Schwefel H-P (eds). Parallel Problem Solving from Nature, PPSN V. Lecture Notes in Computer Science, Springer: Amsterdam, the Netherlands, pp 845-854.
    • Hart E, Ross P and Nelson JAD (1998). Solving a real-world problem using an evolving heuristically driven schedule builder. Evolutionary Computing 6(1): 61-80.
    • He J, He F and Dong H (2012). Pure strategy or mixed strategy?- An initial comparison of their asymptotic convergence rate and asymptotic hitting time. In: Hao JK and Middendorf M (eds). Evolutionary Computation in Combinatorial Optimization-12th European Conference, EvoCOP 2012. Málaga, Spain, 11-13 April 2012. Proceedings, Lecture Notes in Computer Science, Vol. 7245, Springer, pp 218-229.
    • Ho NB and Tay JC (2005). Evolving dispatching rules for solving the flexible job-shop problem. In: IEEE Congress on Evolutionary Computation (CEC'05). IEEE: Edinburgh, UK, pp 2848-2855.
    • Hoos HH and Stützle T (2004). Stochastic Local Search: Foundations and Applications. Elsevier/Morgan Kaufmann: San Francisco, CA.
    • Horvitz E, Ruan Y, Gomes C, Kautz H, Selman B and Chickering D (2001). A bayesian approach to tackling hard computational problems. In: Breese JS and Koller D (eds). Conference in Uncertainty in Artificial Intelligence, UAI '01. Morgan Kaufman: Los Altos, CA, pp 235-244.
    • Huberman BA, Lukose RM and Hogg T (1997). An economics approach to hard computational problems. Science 275(5296): 51-54.
    • Hutter F, Hoos HH and Stützle T (2007). Automatic algorithm configuration based on local search. In: Cohn A (ed). Proceedings of the Twenty-second AAAI Conference on Artificial Intelligence. AAAI Press: Vancouver, British Columbia, Canada, pp 1152-1157.
    • Hutter F, Hoos H, Leyton-Brown K and Stützle T (2009). Paramils: An automatic algorithm configuration framework. Journal of Artificial Intelligence Research (JAIR) 36(1): 267-306.
    • Jakob W (2006). Towards an adaptive multimeme algorithm for parameter optimisation suiting the engineers' needs. In: Runarsson TP, Beyer H-G, Burke E, Merelo-Guervós JJ, Whitley LD, Yao X (eds). Parallel Problem Solving from Nature (PPSN IX). Lecture Notes in Computer Science, Springer: Berlin, pp 132-141.
    • Jakobovic D, Jelenkovic L and Budin L (2007). Genetic programming heuristics for multiple machine scheduling. In: Ebner M, O'Neill M, Ekárt A, Vanneschi L, Esparcia-Alcázar A (eds). European Conference on Genetic Programming (EUROGP' 07). Lecture Notes in Computer Science, Springer: Valencia, Spain, pp 321-330.
    • Kaelbling LP, Littman ML and Moore AW (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research 4(1): 237-285.
    • Kampouridis M, Alsheddy A and Tsang E (2012). On the investigation of hyper-heuristics on a financial forecasting Genetics-based learning of new heuristics: Rational scheduling of experiments and generalization. IEEE Transactions on Knowledge and Data Engineering 7(5): 763-785.
    • Walker JD, Ochoa G, Gendreau M and Burke EK (2012). Vehicle routing and adaptive iterated local search within the hyflex hyper-heuristic framework. In: International Conference on Learning and Intelligent Optimization (LION 6) , Lecture Notes in Computer Science, Vol. 7219, Springer: Berlin, pp 265-276.
    • Wilson S (1995). Classifier systems based on accuracy. Evolutionary Computation 3(2): 149-175.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Cookies make it easier for us to provide you with our services. With the usage of our services you permit us to use cookies.
More information Ok