Tuning the PBIL algorithm to solve a real-world FAP problem

Frequency planning, also known as frequency assignment problem (FAP), is a very important task for current GSM operators. FAP basically tries to minimise the number of interferences (or conflicts in the communications) caused when a limited number of frequencies has to be assigned to a quite high number of transceivers (and there are much more transceivers than frequencies). In this work, we focus on solving this problem for a realistic-sized, real-world GSM network using the population-based incremental learning (PBIL) algorithm. The work described here is divided in two parts. In the first one, we analyse and fix the standard PBIL algorithm to solve the FAP; whereas in the second, we take as initial point the results obtained with the standard version of PBIL and we perform a complete study with the most relevant variations of the algorithm to discover which approach can compute the best frequency plans for real-world instances.