Linear GP for multi-class object classification
Abstract: Multi-class object classification is an important field of research in computer vision. In this paper basic linear genetic programming is modified to be more suitable for multi-class classification and its performance is then compared to tree-based genetic programming. The directed acyclic graph nature of linear genetic programming is exploited. The existing fitness function is modified to more accurately approximate the true feature space. The results show that the new linear genetic programming approach outperforms the basic tree-based genetic programming approach on all the tasks investigated here and that the new fitness function leads to better and more consistent results. The genetic programs evolved by the new linear genetic programming system are also more comprehensible than those evolved by the tree-based system.
@InProceedings{fogelbergZhang05, author = "Christopher Fogelberg and Mengjie Zhang", title = "Linear Genetic Programming for Multi-class Object Classification", booktitle = "AI 2005: Advances in Artificial Intelligence: Proceedings of the 18th Australian Joint Conference on Artificial Intelligence, Lecture Notes in Computer Science, Vol. 3809.", year = "2005", month = "December", editor = "Shichao Zhang and Ray Jarvis", series = "LNAI", volume = "3809", address = "Sydney, Australia", publisher = "Springer Verlag", publisher_address = "Berlin", pages = "369--379", keywords = "linear genetic programming, evolutionary computation, multi-class classification", ISBN = "3-540-30462-2", abstract = "Multi-class object classification is an important field of research in computer vision. In this paper basic linear genetic programming is modified to be more suitable for multi-class classification and its performance is then compared to tree-based genetic programming. The directed acyclic graph nature of linear genetic programming is exploited. The existing fitness function is modified to more accurately approximate the true feature space. The results show that the new linear genetic programming approach outperforms the basic tree-based genetic programming approach on all the tasks investigated here and that the new fitness function leads to better and more consistent results. The genetic programs evolved by the new linear genetic programming system are also more comprehensible than those evolved by the tree-based system.", note = "Available from {\tt{http://syntilect.com/pubs/aaai2005}}" }
Available here.