TY - GEN
T1 - Improving maritime awareness with semantic genetic programming and linear scaling
T2 - 18th European Conference on the Applications of Evolutionary Computation, EvoApplications 2015
AU - Vanneschi, Leonardo
AU - Castelli, Mauro
AU - Costa, Ernesto
AU - Re, Alessandro
AU - Vaz, Henrique
AU - Lobo, Vítor
AU - Urbano, Paulo
N1 - Vanneschi, L., Castelli, M., Costa, E., Re, A., Vaz, H., Lobo, V., & Urbano, P. (2015). Improving maritime awareness with semantic genetic programming and linear scaling: Prediction of vessels position based on AIS data. In Applications of Evolutionary Computation - 18th European Conference, EvoApplications 2015, Proceedings (Vol. 9028, pp. 732-744). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9028). Springer-Verlag. https://doi.org/10.1007/978-3-319-16549-3_59
PY - 2015
Y1 - 2015
N2 - Maritime domain awareness deals with the situational understanding of maritime activities that could impact the security, safety, economy or environment. It enables quick threat identification, informed decision making, effective action support, knowledge sharing and more accurate situational awareness. In this paper, we propose a novel computational intelligence framework, based on genetic programming, to predict the position of vessels, based on information related to the vessels past positions in a specific time interval. Given the complexity of the task, two well known improvements of genetic programming, namely geometric semantic operators and linear scaling, are integrated in a new and sophisticated genetic programming system. The work has many objectives, for instance assisting more quickly and effectively a vessel when an emergency arises or being able to chase more efficiently a vessel that is accomplishing illegal actions. The proposed system has been compared to two different versions of genetic programming and three non-evolutionary machine learning methods, outperforming all of them on all the studied test cases.
AB - Maritime domain awareness deals with the situational understanding of maritime activities that could impact the security, safety, economy or environment. It enables quick threat identification, informed decision making, effective action support, knowledge sharing and more accurate situational awareness. In this paper, we propose a novel computational intelligence framework, based on genetic programming, to predict the position of vessels, based on information related to the vessels past positions in a specific time interval. Given the complexity of the task, two well known improvements of genetic programming, namely geometric semantic operators and linear scaling, are integrated in a new and sophisticated genetic programming system. The work has many objectives, for instance assisting more quickly and effectively a vessel when an emergency arises or being able to chase more efficiently a vessel that is accomplishing illegal actions. The proposed system has been compared to two different versions of genetic programming and three non-evolutionary machine learning methods, outperforming all of them on all the studied test cases.
UR - http://www.scopus.com/inward/record.url?scp=84925860453&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-16549-3_59
DO - 10.1007/978-3-319-16549-3_59
M3 - Conference contribution
AN - SCOPUS:84925860453
VL - 9028
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 732
EP - 744
BT - Applications of Evolutionary Computation - 18th European Conference, EvoApplications 2015, Proceedings
PB - Springer Verlag
Y2 - 8 April 2015 through 10 April 2015
ER -