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Population-Based Incremental Learning to Solve the FAP Problem

机译:基于人口的增量学习来解决FAP问题

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摘要

Frequency assignment problem (FAP) is a very important issue in the field of telecommunications (especially in GSM -Global System for Mobile-Networks). In this work, we present the Population-Based Incremental Learning (PBIL) algorithm to solve a particular branch of the FAP problem (MS-FAP). MS-FAP (Minimum Span Frequency Assignment Problem) tries to minimize the range of frequencies which is necessary in a certain area to cover the communications which take place there. In this paper it is presented the problem and it is explained the methodology which solve it. We have performed tests with a complete set of experiments using seven well-known variations of PBIL and 7 types of MS-FAP problems. At the end, the results are presented and we compare them to conclude which variation of PBIL provides the best solution to the MS-FAP problem.
机译:频率分配问题(FAP)是电信领域的一个非常重要的问题(特别是在移动网络的GSM-Global系统中)。在这项工作中,我们介绍了基于人口的增量学习(PBIL)算法来解决FAP问题的特定分支(MS-FAP)。 MS-FAP(最小跨度频率分配问题)尝试最小化某个区域所需的频率范围,以覆盖在那里进行的通信。在本文中,介绍了问题,并解释了解决它的方法。我们使用了一整套实验进行了测试,使用七种已知的PBIL变化和7种MS-FAP问题。最后,提出了结果,并将它们与他们得出结论,Pbil的哪种变化为MS-FAP问题提供了最佳解决方案。

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