Figure of Merit Based Fitness Functions in Genetic Programming for Edge Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Fu:2012:SEAL,

• author = "Wenlong Fu and Mark Johnston and Mengjie Zhang",
• title = "Figure of Merit Based Fitness Functions in Genetic Programming for Edge Detection",
• booktitle = "The Ninth International Conference on Simulated Evolution And Learning, SEAL 2012",
• year = "2012",
• editor = "Lam Thu Bui and Yew-Soon Ong and Nguyen Xuan Hoai and Hisao Ishibuchi and Ponnuthurai Nagaratnam Suganthan",
• volume = "7673",
• series = "Lecture Notes in Computer Science",
• pages = "22--31",
• address = "Vietnam",
• month = dec # " 16-19",
• organisation = "Faculty of Information Technology, Le Quy Don Technical University",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Edge Detection, Figure of Merit",
• isbn13 = "978-3-642-34858-7",
• DOI = "doi:10.1007/978-3-642-34859-4_3",
• size = "10 pages",
• abstract = "The figure of merit (FOM) is popular for testing an edge detector's performance, but there are very few reports using FOM as an evaluation method in Genetic Programming (GP). In this study, FOM is investigated as a fitness function in GP for edge detection. Since FOM has some drawbacks from type II errors, new fitness functions are developed based on FOM in order to address these weaknesses. Experimental results show that FOM can be used to evolve GP edge detectors that perform better than the Sobel detector, and the new fitness functions clearly improve the ability of GP edge detectors to find edge points and give a single response on edges, compared with the fitness function using FOM.",

}

Genetic Programming entries for Wenlong Fu Mark Johnston Mengjie Zhang

Citations