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 given 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 in the following extended with a knowledge reuse mechanism that allows a learner to import genetic material from hypotheses that evolved for 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 significant convergence speedup and reduces the risk of overfitting.
@INPROCEEDINGS { jaskowski07knowledge,
AUTHOR = { Wojciech Ja\'skowski and Krzysztof Krawiec and Bartosz Wieloch },
TITLE = { Knowledge Reuse for an Ensemble of GP-Based Learners },
BOOKTITLE = { Evolutionary Computation and Global Optimization 2007 },
YEAR = { 2007 },
EDITOR = { Jaroslaw Arabas },
VOLUME = { 160 },
SERIES = { Prace Naukowe Politechniki Warszawskiej },
PAGES = { 135--142 },
ADDRESS = { Bedlewo, Poland },
MONTH = { jun },
PUBLISHER = { Oficyna Wydawnicza Politechniki Warszawskiej },
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 given 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 in the following extended with a knowledge reuse mechanism that allows a learner to import genetic material from hypotheses that evolved for 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 significant convergence speedup and reduces the risk of overfitting. },
FILE = { jaskowski07knowledge.pdf:\\jaskowski07knowledge.pdf:PDF },
URL = { http://www.cs.put.poznan.pl/wjaskowski },
KEYWORDS = { evolutionary computation, genetic programming, knowledge reuse, visual learning, character recognition },
TIMESTAMP = { 2007.05.11 },
}