CellNet Co-Ev: Evolving Better Pattern Recognizers Using Competitive Co-evolution Export

@incollection{citeulike:2651463, abstract = {A model for the co-evolution of patterns and classifiers is presented. The CellNet system for generating binary classifiers is used as a base for experimentation. The CellNet system is extended to include a competitive coevolutionary Genetic Algorithm, where patterns evolve as well as classifiers; This is facilitated by the addition of a set of topologically-invariant camouflage functions, through which images may disguise themselves. This allows for the creation of a larger and more varied image database, and also artificially increases the difficulty of the classification problem. Application to the CEDAR database of hand-written characters yields both an increase in reliability and an elimination of over-fitting relative to the original CellNet project.}, author = {Kowaliw, Taras and Kharma, Nawwaf and Jensen, Chris and Moghnieh, Hussein and Yao, Jie}, citeulike-article-id = {2651463}, citeulike-linkout-0 = {http://www.springerlink.com/content/uw9mbhc8wea39gx3}, citeulike-linkout-1 = {http://kowaliw.net/papers/kowaliw_cellnet_gecco04.pdf}, citeulike-linkout-2 = {http://users.encs.concordia.ca/~kharma/ResearchWeb/html/research/cellNet.html}, journal = {Genetic and Evolutionary Computation – GECCO 2004}, keywords = {camouflage, coevolution, ga, recognition, texture\_analysis, vision}, pages = {1090--1101}, posted-at = {2008-04-11 04:08:49}, priority = {0}, title = {CellNet Co-Ev: Evolving Better Pattern Recognizers Using Competitive Co-evolution}, url = {http://www.springerlink.com/content/uw9mbhc8wea39gx3}, year = {2004} }

TY - CHAP ID - citeulike:2651463 L3 - citeulike-article-id:2651463 N2 - A model for the co-evolution of patterns and classifiers is presented. The CellNet system for generating binary classifiers is used as a base for experimentation. The CellNet system is extended to include a competitive coevolutionary Genetic Algorithm, where patterns evolve as well as classifiers; This is facilitated by the addition of a set of topologically-invariant camouflage functions, through which images may disguise themselves. This allows for the creation of a larger and more varied image database, and also artificially increases the difficulty of the classification problem. Application to the CEDAR database of hand-written characters yields both an increase in reliability and an elimination of over-fitting relative to the original CellNet project. JF - Genetic and Evolutionary Computation – GECCO 2004 EP - 1101 TI - CellNet Co-Ev: Evolving Better Pattern Recognizers Using Competitive Co-evolution SP - 1090 KW - camouflage KW - coevolution KW - ga KW - recognition KW - texture_analysis KW - vision AU - Kowaliw, Taras AU - Kharma, Nawwaf AU - Jensen, Chris AU - Moghnieh, Hussein AU - Yao, Jie PY - 2004/// UR - http://www.springerlink.com/content/uw9mbhc8wea39gx3 ER -