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Linear Genetic Programming for Multi-class Object Classification

  • Christopher Fogelberg
  • Mengjie Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)

Abstract

Multi-class object classification is an important field of research in computer vision. In this paper basic linear genetic programming is modified to be more suitable for multi-class classification and its performance is then compared to tree-based genetic programming. The directed acyclic graph nature of linear genetic programming is exploited. The existing fitness function is modified to more accurately approximate the true feature space. The results show that the new linear genetic programming approach outperforms the basic tree-based genetic programming approach on all the tasks investigated here and that the new fitness function leads to better and more consistent results. The genetic programs evolved by the new linear genetic programming system are also more comprehensible than those evolved by the tree-based system.

Keywords

Genetic Programming Directed Acyclic Graph Shape Data Training Accuracy Linear Genetic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Christopher Fogelberg
    • 1
  • Mengjie Zhang
    • 1
  1. 1.School of Mathematics, Statistics and Computer SciencesVictoria University of WellingtonWellingtonNew Zealand

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