Abstract
Hyper-Heuristics is a recent area of research concerned with the automatic design of algorithms. In this paper we propose a grammar-based hyper-heuristic to automate the design of an Evolutionary Algorithm component, namely the parent selection mechanism. More precisely, we present a grammar that defines the number of individuals that should be selected, and how they should be chosen in order to adjust the selective pressure. Knapsack Problems are used to assess the capacity to evolve selection strategies. The results obtained show that the proposed approach is able to evolve general selection methods that are competitive with the ones usually described in the literature.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Burke, E., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.: A classification of hyper-heuristic approaches. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics, International Series in Operations Research and Management Science, vol. 146, pp. 449–468. Springer, US (2010)
Group, N.W.: Augmented BNF for syntax specifications: ABNF (1 2008). ftp://ftp.rfc-editor.org/in-notes/std/std68.txt
Keller, R., Poli, R.: Linear genetic programming of parsimonious metaheuristics. In: IEEE Congress on Evolutionary Computation, 2007, CEC 2007, pp. 4508–4515 (2007)
Kellerer, H., Pferschy, U., Pisinger, D.: Knapsack Problems. Springer, Berlin (2004)
Lourenço, N., Pereira, F., Costa, E.: Evolving evolutionary algorithms. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion, GECCO ’12, pp. 51–58. ACM, New York (2012)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, London (1996)
O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers, Norwell (2003)
Pappa, G.L., Freitas, A.: Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach, 1st edn. Springer, Berlin (2009)
Raidl, G.R., Gottlieb, J.: Empirical analysis of locality, heritability and heuristic bias in evolutionary algorithms: A case study for the multidimensional knapsack problem. Evol. Comput. 13(4), 441–475 (2005)
Ross, P.: Hyper-heuristics. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Chap. 17, pp. 529–556. Springer, US (2005)
Tavares, J., Machado, P., Cardoso, A., Pereira, F.B., Costa, E.: On the evolution of evolutionary algorithms. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 389–398. Springer, Heidelberg (2004)
Tavares, J., Pereira, F.B.: Automatic design of ant algorithms with grammatical evolution. In: Proceedings of the 15th European Conference on Genetic Programming (2012)
Woodward, J., Swan, J.: Automatically designing selection heuristics. In: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO ’11, pp. 583–590. ACM, New York (2011)
Acknowledgments
This work was partially supported by Fundação para a Ciência e Tecnologia (FCT), Portugal, under the grant SFRH/BD/79649/2011.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Lourenço, N., Pereira, F., Costa, E. (2014). Learning Selection Strategies for Evolutionary Algorithms. In: Legrand, P., Corsini, MM., Hao, JK., Monmarché, N., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2013. Lecture Notes in Computer Science(), vol 8752. Springer, Cham. https://doi.org/10.1007/978-3-319-11683-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-11683-9_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11682-2
Online ISBN: 978-3-319-11683-9
eBook Packages: Computer ScienceComputer Science (R0)