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Chapter

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Title

Coevolution and Linear Genetic Programming for Visual Learning

Authors

[ 1 ] Instytut Informatyki (II), Wydział Informatyki i Zarządzania, Politechnika Poznańska | [ P ] employee

Year of publication

2003

Chapter type

paper

Publication language

english

Abstract

EN In this paper, a novel genetically-inspired visual learning method is proposed. Given the training images, this general approach induces a sophisticated feature-based recognition system, by using cooperative coevolution and linear genetic programming for the procedural representation of feature extraction agents. The paper describes the learning algorithm and provides a firm rationale for its design. An extensive experimental evaluation, on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery, shows the competitiveness of the proposed approach with human-designed recognition systems.

Pages (from - to)

332 - 343

DOI

10.1007/3-540-45105-6_39

URL

https://link.springer.com/chapter/10.1007/3-540-45105-6_39

Book

Genetic and Evolutionary Computation - GECCO 2003 : Genetic and Evolutionary Computation Conference, Chicago, IL, USA, July 12-16, 2003 : Proceedings, Part I

Presented on

Genetic and Evolutionary Computation GECCO 2003, 12-16.07.2003, Chicago, United States

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