MaSSGP - Improving Semantic Genetic Programming for Maritime Safety, Security and Environmental Protection
Description
The goals of the project are: (1) to develop a Genetic Programming (GP) system that improves the state-of-the-art methods on classification and regression problems; (2) to integrate this system in an industrial decision support tool; (3) to use this system to generate new insightful models for maritime safety, security and environmental protection applications.
Researchers
Keywords
Evolutionary Computation, Genetic Programming, Maritime Safety, Environmental Protection
Start Date
2013-04-01
End Date
2015-03-31
Journal Articles
Conference Articles
2015
(3 publications) - Ivo Gonçalves and Sara Silva and Fonseca, C.M. , "On the Generalization Ability of Geometric Semantic Genetic Programming", in 18th European Conference on Genetic Programming (EuroGP 2015), 2015
- Ivo Gonçalves and Sara Silva and Fonseca, C.M. , "Semantic Learning Machine: A Feedforward Neural Network Construction Algorithm Inspired by Geometric Semantic Genetic Programming", in 17th Portuguese Conference on Artificial Intelligence (EPIA 2015), 2015
- Vanneschi, L. and Castelli, M. and Costa, E. and Vaz, H. and Lobo, V. and Urbano, P. , "Improving maritime awareness with semantic genetic programming and linear scaling: prediction of vessels positions based on AIS data", in EvoAPPS, 2015
Book Chapters