Abstract
Whenever a new problem needs to be tackled, one needs to decide which of the many existing metaheuristics would be the most adequate one; but it is very difficult to know their performance a priori. And then, when a metaheuristic is chosen, there are still its parameters that need to be set by the user. This parameter setting is usually very problem-dependent, significantly affecting their performance. In this work we propose the use of an Adaptive Operator Selection (AOS) mechanism to automatically control, while solving the problem, (i) which metaheuristic to use for the generation of a new solution, (exemplified here by a Genetic Algorithm (GA) and a Differential Evolution (DE) scheme); and (ii) which corresponding operator should be used, (selecting among five operators available for the GA and four operators for DE). Two AOS schemes are considered: the Adaptive Pursuit and the Fitness Area Under Curve Multi-Armed Bandit. The resulting algorithm, named as Adaptive Hyper-Heuristic (HH), is evaluated on the BBOB noiseless testbed, showing superior performance when compared to (a) the same HH without adaptation, and also (b) the adaptive DE and GA.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multi-armed bandit problem. Machine Learning 47(2-3), 235–256 (2002)
Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Qu, R.: Hyper-heuristics: A survey of the state of the art. Tech. rep., U. of Nottingham (2010)
Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-schemata. In: Foundation of Genetic Algorithms 2 (1993)
Fialho, Á., Da Costa, L., Schoenauer, M., Sebag, M.: Extreme Value Based Adaptive Operator Selection. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 175–184. Springer, Heidelberg (2008)
Fialho, Á., Schoenauer, M., Sebag, M.: Toward comparison-based adaptive operator selection. In: Proc. Genetic Evol. Comput. Conf. (2010)
Fialho, Á.: Adaptive Operator Selection for Optimization. Ph.D. thesis, Université Paris-Sud XI, Orsay, France (December 2010)
Hansen, N., Auger, A., Finck, S., Ros, R.: Real-parameter black-box optimization benchmarking: Noiseless functions definitions. RR-6829. Tech. rep., INRIA (2009)
Hansen, N., Auger, A., Finck, S., Ros, R.: Real-parameter black-box optimization benchmarking 2010: Experimental setup. RR-7215. Tech. rep., INRIA (2010)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolutionary Programs. Springer (1992)
Özcan, E., Bilgin, B., Korkmaz, E.E.: A comprehensive analysis of hyperheuristics. Intell. Data Anal. 12(1), 3–20 (2008)
Storn, R., Price, K.V.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. of Global Opt. 11, 341–359 (1997)
Sywerda, G.: Uniform crossover in genetic algorithms. In: Proc. of the Third Int. Conf. on Genetic Algorithms, San Francisco, CA, USA, pp. 2–9 (1989)
Thierens, D.: An adaptive pursuit strategy for allocating operator probabilities. In: Proc. GECCO, pp. 1539–1546 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Krempser, E., Fialho, Á., Barbosa, H.J.C. (2012). Adaptive Operator Selection at the Hyper-level. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-32964-7_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32963-0
Online ISBN: 978-3-642-32964-7
eBook Packages: Computer ScienceComputer Science (R0)