@inproceedings{Krawiec-chapter:2001,
	title = {Genetic Programming with Local Improvement for Visual
                 Learning from Examples},
	address = {Warsaw, Poland},
	author = {Krzysztof Krawiec},
	booktitle = {Proceedings 9th International Conference on Computer
                 Analysis of Images and Patterns, CAIP 2001},
	editor = {W. Skarbek},
	month = {September 5-7},
	pages = {209--216},
	publisher = {Springer-Verlag},
	series = {Lecture Notes in Computer Science},
	url = {http://link.springer.de/link/service/series/0558/bibs/2124/21240209.htm},
	volume = {2124},
	year = {2001},
	biburl = {http://www.bibsonomy.org/bibtex/2d2e819ebf3188b9347147e7ea1def9cb/brazovayeye},
	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.},
	issn = {0302-9743}, publisher_address = {Heidelberg}, notes = {A1 Institute of Computing Science, Poznan University
                 of Technology,Piotrowo 3A, 60965 Poznan, Poland
                 krawiec@cs.put.poznan.pl},
	keywords = {algorithms, examples from genetic learning learning, local programming, search, visual }
}