Coevolution and linear genetic programming for visual learning

Authors:
Krzysztof Krawiec;Bir Bhanu
Affiliations:
Center for Research in Intelligent Systems, University of California, Riverside, CA;Center for Research in Intelligent Systems, University of California, Riverside, CA
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Year:
2003

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Abstract

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.