- author = "Andreas Konstantinidis and Haris Haralambous and Alexandros Agapitos and Harris Papadopoulos",
- title = "A GP-MOEA/D approach for modelling total electron content over Cyprus",
- journal = "International Journal of Engineering Intelligent Systems",
- year = "2010",
- volume = "18",
- number = "3/4",
- pages = "193--203",
- month = sep # "/" # dec,
- note = "Special issue Artificial Intelligence Applications and Innovations 2010 Lazaros Iliadis, Andreas Andreou and Harris Papadopoulos",
- email = "eis@crlpublishing.co.uk admin@crlpublishing.co.uk",
- publisher = "CRL 9 Priory Business Park, Wistow Road, Kibworth Beauchamp, Leicester, LE8 0RX | Tel: +44 116 279 2299 | Fax: +44 116 279 2277",
- keywords = "genetic algorithms, genetic programming, time-series prediction, multi-objective evolutionary algorithms",
- ISSN = "1472-8915",
-
URL = "
http://www.crlpublishing.co.uk/journal.asp?j=eis&s=Vol%2018%202010",
-
URL = "http://arxiv.org/abs/1111.5720",
- size = "11 pages",
- abstract = "Vertical Total Electron Content (vTEC) is an ionospheric characteristic used to derive the signal delay imposed by the ionosphere on near-vertical trans-ionospheric links. The major aim of this paper is to design a prediction model based on the main factors that influence the variability of this parameter on a diurnal, seasonal and long-term time-scale. The model should be accurate and general (comprehensive) enough for efficiently approximating the high variations of vTEC. However, good approximation and generalisation are conflicting objectives. For this reason a Genetic Programming (GP) with Multi-objective Evolutionary Algorithm based on Decomposition characteristics (GP-MOEA/D) is designed and proposed for modelling vTEC over Cyprus. Experimental results show that the Multi-Objective GP-model, considering real vTEC measurements obtained over a period of 11 years, has produced a good approximation of the modelled parameter and can be implemented as a local model to account for the ionospheric imposed error in positioning. Particularly, the GP-MOEA/D approach performs better than a Single Objective Optimisation GP, a GP with Non-dominated Sorting Genetic Algorithm-II (NSGA-II) characteristics and the previously proposed Neural Network-based approach in most cases.",
-
notes = "AIAI 2010 paper is \cite{Agapitos:2010:AIAI}.
Curtin University of Technology, Australia",
