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Genetic Programming with Local Improvement for Visual Learning from Examples

  • Krzysztof Krawiec
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2124)

Abstract

This paper investigates the use of evolutionary programming for the search of hypothesis space in visual learning tasks. The general goal of the project is to elaborate human-competitive procedures for pattern discrimination by means of learning based on the training data (set of images). In particular, the topic addressed here is the comparison between the ‘standard’ genetic programming (as defined by Koza [13]) and the genetic programming extended by local optimization of solutions, so-called genetic local search. The hypothesis formulated in the paper is that genetic local search provides better solutions (i.e. classifiers with higher predictive accuracy) than the genetic search without that extension. This supposition was positively verified in an extensive comparative experiment of visual learning concerning the recognition of handwritten characters.

Keywords

visual learning genetic local search learning from examples 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Krzysztof Krawiec
    • 1
  1. 1.Institute of Computing SciencePoznań University of TechnologyPoznańPoland

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