Abstract
Designing customized optimization problem instances is a key issue in optimization. They can be used to tune and evaluate new algorithms, to compare several optimization algorithms, or to evaluate techniques that estimate the number of local optima of an instance. Given this relevance, several methods have been proposed to design customized optimization problems in the field of evolutionary computation for continuous as well as binary domains. However, these proposals have not been extended to permutation spaces. In this paper we provide a method to generate customized landscapes in permutation-based combinatorial optimization problems. Based on a probabilistic model for permutations, called the Mallows model, we generate instances with specific characteristics regarding the number of local optima or the sizes of the attraction basins.
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Acknowledgements.
This work has been partially supported by the Saiotek, Etortek and Research Groups 2007-2012 (IT- 242-07) programs (Basque Government), TIN2010-14931 (Spanish Ministry of Science and Innovation) and COMBIOMED network in computational biomedicine (Carlos III Health Institute). Leticia Hernando holds a grant from the Basque Government.
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Hernando, L., Mendiburu, A., Lozano, J.A. (2013). Generating Customized Landscapes in Permutation-Based Combinatorial Optimization Problems. In: Nicosia, G., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science(), vol 7997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44973-4_33
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DOI: https://doi.org/10.1007/978-3-642-44973-4_33
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