Knowledge Reuse for an Ensemble of GP-based Learners

by Wojciech Jaśkowski, Krzysztof Krawiec, Bartosz Wieloch
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.
Reference:
Knowledge Reuse for an Ensemble of GP-based Learners (Wojciech Jaśkowski, Krzysztof Krawiec, Bartosz Wieloch), In Evolutionary Computation and Global Optimization 2007 (Jaroslaw Arabas, ed.), Oficyna Wydawnicza Politechniki Warszawskiej, volume 160, 2007.
Bibtex Entry:
@InProceedings{Jaskowski2007knowledge,
  Title                    = {Knowledge Reuse for an Ensemble of {GP}-based Learners},
  Author                   = {Wojciech Jaśkowski and Krzysztof Krawiec and Bartosz Wieloch},
  Booktitle                = {Evolutionary Computation and Global Optimization 2007},
  Year                     = {2007},

  Address                  = {Bedlewo, Poland},
  Editor                   = {Jaroslaw Arabas},
  Month                    = {jun},
  Pages                    = {135--142},
  Publisher                = {Oficyna Wydawnicza Politechniki Warszawskiej},
  Series                   = {Prace Naukowe Politechniki Warszawskiej},
  Volume                   = {160},

  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},
  Homepage-url             = {http://www.cs.put.poznan.pl/wjaskowski},
  Keywords                 = {evolutionary computation, genetic programming, knowledge reuse, visual learning, character recognition},
  Url                      = {http://www.cs.put.poznan.pl/wjaskowski/pub/papers/jaskowski07knowledge.pdf}
}

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