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2014 | 24 | 1 | 183-197
Tytuł artykułu

Cross-task code reuse in genetic programming applied to visual learning

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We propose a method that enables effective code reuse between evolutionary runs that solve a set of related visual learning tasks. We start with introducing a visual learning approach that uses genetic programming individuals to recognize objects. The process of recognition is generative, i.e., requires the learner to restore the shape of the processed object. This method is extended with a code reuse mechanism by introducing a crossbreeding operator that allows importing the genetic material from other evolutionary runs. In the experimental part, we compare the performance of the extended approach to the basic method on a real-world task of handwritten character recognition, and conclude that code reuse leads to better results in terms of fitness and recognition accuracy. Detailed analysis of the crossbred genetic material shows also that code reuse is most profitable when the recognized objects exhibit visual similarity.
Rocznik
Tom
24
Numer
1
Strony
183-197
Opis fizyczny
Daty
wydano
2014
otrzymano
2013-04-18
poprawiono
2013-09-28
poprawiono
2013-11-07
Twórcy
  • Institute of Computing Science, Poznań University of Technology, ul. Piotrowo 2, 60-965 Poznań, Poland
  • Institute of Computing Science, Poznań University of Technology, ul. Piotrowo 2, 60-965 Poznań, Poland
  • Institute of Computing Science, Poznań University of Technology, ul. Piotrowo 2, 60-965 Poznań, Poland
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv24i1p183bwm
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