Abstract
This work proposes an Estimation of Distribution Algorithm (EDA) that incorporates an explicit separation between the exploration stage and the exploitation stage. For each stage a probabilistic model is required. The proposed EDA uses a mixture of distributions in the exploration stage whereas a multivariate Gaussian distribution is used in the exploitation stage. The benefits of using an explicit exploration stage are shown through numerical experiments.
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Salinas-Gutiérrez, R., Muñoz-Zavala, Á.E., Hernández-Aguirre, A., Castillo-Galván, M.A. (2013). Explicit Exploration in Estimation of Distribution Algorithms. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Soft Computing and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45111-9_7
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DOI: https://doi.org/10.1007/978-3-642-45111-9_7
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