Title
Radio Network Design Using Population-Based Incremental Learning and Grid Computing with BOINC
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.
Year
DOI
Venue
2007
10.1007/978-3-540-71805-5_10
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Keywords
DocType
Volume
biggest cover rate,grid computing,pbil,boinc,evolutionary algorithm,antenna,smallest number,grid computing.,high number,radio network design,important problem,coverage,population-based incremental learning,optimization problem,rnd problem,rnd,berkeley open infrastructure,telecommunications problem,network computing,genetic algorithm,competitive learning,neural network,probabilistic model
Conference
4448
ISSN
Citations 
PageRank 
0302-9743
6
1.00
References 
Authors
6
4