Genetic Programming with Local Improvement for Visual Learning from Examples

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Krawiec-chapter:2001,

• author = "Krzysztof Krawiec",
• year = "2001",
• title = "Genetic Programming with Local Improvement for Visual Learning from Examples",
• booktitle = "Proceedings 9th International Conference on Computer Analysis of Images and Patterns, CAIP 2001",
• editor = "W. Skarbek",
• volume = "2124",
• series = "Lecture Notes in Computer Science",
• pages = "209--216",
• address = "Warsaw, Poland",
• publisher_address = "Heidelberg",
• month = sep # " 5-7",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming, visual learning, genetic local search, learning from examples",
• ISSN = "0302-9743",
• DOI = "doi:10.1007/3-540-44692-3_26",
• 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.",
• notes = "A1 Institute of Computing Science, Poznan University of Technology, Piotrowo 3A, 60965 Poznan, Poland krawiec@cs.put.poznan.pl",

}

Genetic Programming entries for Krzysztof Krawiec

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