Synthesizing feature agents using evolutionary computation

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

@Article{Bhanu:2004:PRL,

• author = "Bir Bhanu and Yingqiang Lin",
• title = "Synthesizing feature agents using evolutionary computation",
• journal = "Pattern Recognition Letters",
• year = "2004",
• volume = "25",
• pages = "1519--1531",
• number = "13",
• abstract = "genetic programming (GP) with smart crossover and smart mutation is proposed to discover integrated feature agents that are evolved from combinations of primitive image processing operations to extract regions-of-interest (ROIs) in remotely sensed images. The motivation for using genetic programming is to overcome the limitations of human experts, since GP attempts many unconventional ways of combination, in some cases, these unconventional combinations yield exceptionally good results. Smart crossover and smart mutation identify and keep the effective components of integrated operators called {"}agents{"} and significantly improve the efficiency of GP. Our experimental results show that compared to normal GP, our GP algorithm with smart crossover and smart mutation can find good agents more quickly during training to effectively extract the regions-of-interest and the learned agents can be applied to extract ROIs in other similar images.",
• owner = "wlangdon",
• URL = "http://www.sciencedirect.com/science/article/B6V15-4CRY8J6-2/2/d245bfcfeee2d509066321e19d84a0fd",
• month = "1 " # oct,
• note = "Pattern Recognition for Remote Sensing (PRRS 2002)",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1016/j.patrec.2004.06.005",
• size = "13 pages",
• notes = "SAR",

}

Genetic Programming entries for Bir Bhanu Yingqiang Lin

Citations