We propose a method of knowledge reuse for an ensemble of genetic programming-based learners solving a visual learning task. First, we introduce a visual learning method that uses genetic programming individuals to represent hypotheses. Individuals-hypotheses process image representation composed of visual primitives derived from the training images that contain objects to be recognized. The process of recognition is generative, i.e., an individual is supposed to restore the shape of the processed object by drawing its reproduction on a separate canvas. This canonical method is extended with a knowledge reuse mechanism that allows a learner to import genetic material from hypotheses that evolved for the other decision classes (object classes). We compare the performance of the extended approach to the basic method on a real-world tasks of handwritten character recognition, and conclude that knowledge reuse leads to signifcant convergence speedup and, more importantly, significantly reduces the risk of overfitting.
@INPROCEEDINGS { Jaskowski07geccoGBML,
ABSTRACT = { We propose a method of knowledge reuse for an ensemble of genetic programming-based learners solving a visual learning task. First, we introduce a visual learning method that uses genetic programming individuals to represent hypotheses. Individuals-hypotheses process image representation composed of visual primitives derived from the training images that contain objects to be recognized. The process of recognition is generative, i.e., an individual is supposed to restore the shape of the processed object by drawing its reproduction on a separate canvas. This canonical method is extended with a knowledge reuse mechanism that allows a learner to import genetic material from hypotheses that evolved for the other decision classes (object classes). We compare the performance of the extended approach to the basic method on a real-world tasks of handwritten character recognition, and conclude that knowledge reuse leads to signifcant convergence speedup and, more importantly, significantly reduces the risk of overfitting. },
AUTHOR = { Wojciech Ja{\'s}kowski and Krzysztof Krawiec and Bartosz Wieloch },
BOOKTITLE = { Genetic and Evolutionary Computation Conference GECCO },
EDITOR = { Dirk Thierens },
ISBN = { 978-1-59593-698-1 },
PAGES = { 1790--1797 },
PUBLISHER = { Association for Computing Machinery },
TITLE = { Knowledge Reuse in Genetic Programming applied to Visual Learning },
URL = { http://www.cs.put.poznan.pl/kkrawiec/pubs/p1790.pdf },
YEAR = { 2007 },
1 = { http://www.cs.put.poznan.pl/kkrawiec/pubs/p1790.pdf },
}