Abstract
Radio Network Design (RND) is a Telecommunications problem that tries to cover a certain geographical area by using the smallest number of radio antennas, and looking for the biggest cover rate. Therefore, it is an important problem, for example, in mobile/cellular technology. RND can be solved by bio-inspired algorithms, among other options, because it is an optimization problem. In this work we use the PBIL (Population-Based Incremental Learning) algorithm, that has been little studied in this field but we have obtained very good results with it. PBIL is based on genetic algorithms and competitive learning (typical in neural networks), being a new population evolution model based on probabilistic models. Due to the high number of configuration parameters of the PBIL, and because we want to test the RND problem with numerous variants, we have used grid computing with BOINC (Berkeley Open Infrastructure for Network Computing). In this way, we have been able to execute thousands of experiments in only several days using around 100 computers at the same time. In this paper we present the most interesting results from our work.
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Vega-Rodríguez, M.A., Vega-Pérez, D., Gómez-Pulido, J.A., Sánchez-Pérez, J.M. (2007). Radio Network Design Using Population-Based Incremental Learning and Grid Computing with BOINC. In: Giacobini, M. (eds) Applications of Evolutionary Computing. EvoWorkshops 2007. Lecture Notes in Computer Science, vol 4448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71805-5_10
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DOI: https://doi.org/10.1007/978-3-540-71805-5_10
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