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.
Keywords
- Learning Strategy Selection
- Evolutionary Survival Strategy
- Evolutionary Algorithms Components
- Parent Selection Mechanism
- Alternative Steady States
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgments
This work was partially supported by Fundação para a Ciência e Tecnologia (FCT), Portugal, under the grant SFRH/BD/79649/2011.
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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
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DOI: https://doi.org/10.1007/978-3-319-11683-9_16
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