Paper Info
Title
Towards better generalization in WLAN positioning systems with genetic algorithms and neural networks
Abstract
ABSTRACTThe most widely used positioning system today is the GPS (Global Positioning System), which has many commercial, civil and military applications, being present in most smartphones. However, this system does not perform well in indoor locations, which poses a constraint for the positioning task on environments like shopping malls, office buildings, and other public places. In this context, WLAN positioning systems based on fingerprinting have attracted a lot of attention as a promising approach for indoor localization while using the existing infrastructure. This paper contributes to this field by presenting a methodology for developing WLAN positioning systems using genetic algorithms and neural networks. The fitness function of the genetic algorithm is based on the generalization capabilities of the network for test points that are not included in the training set. By using this approach, we have achieved state-of-the-art results with few parameters, and our method has shown to be less prone to overfitting than other techniques in the literature, showing better generalization in points that are not recorded on the radio map.
Year
DOI
Venue
2019
10.1145/3321707.3321712
Genetic and Evolutionary Computation Conference
Keywords
Field
DocType
WLAN positioning systems, indoor localization, neuroevolution, neural architecture search, genetic algorithms, fingerprinting
Training set,Computer science,Fitness function,Artificial intelligence,Global Positioning System,Overfitting,Artificial neural network,Neuroevolution,Machine learning,Genetic algorithm,Positioning system
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Diogo M. F. Izidio100.34
Antonyus Pyetro do Amaral Ferreira283.21
Edna Natividade da Silva Barros35911.11