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

Wojciech Jaśkowski; Krzysztof Krawiec; Bartosz Wieloch

International Journal of Applied Mathematics and Computer Science (2014)

  • Volume: 24, Issue: 1, page 183-197
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Wojciech Jaśkowski, Krzysztof Krawiec, and Bartosz Wieloch. "Cross-task code reuse in genetic programming applied to visual learning." International Journal of Applied Mathematics and Computer Science 24.1 (2014): 183-197. <http://eudml.org/doc/271915>.

@article{WojciechJaśkowski2014,
abstract = {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.},
author = {Wojciech Jaśkowski, Krzysztof Krawiec, Bartosz Wieloch},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {genetic programming; code reuse; knowledge sharing; visual learning; multi-task learning; optical character recognition},
language = {eng},
number = {1},
pages = {183-197},
title = {Cross-task code reuse in genetic programming applied to visual learning},
url = {http://eudml.org/doc/271915},
volume = {24},
year = {2014},
}

TY - JOUR
AU - Wojciech Jaśkowski
AU - Krzysztof Krawiec
AU - Bartosz Wieloch
TI - Cross-task code reuse in genetic programming applied to visual learning
JO - International Journal of Applied Mathematics and Computer Science
PY - 2014
VL - 24
IS - 1
SP - 183
EP - 197
AB - 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.
LA - eng
KW - genetic programming; code reuse; knowledge sharing; visual learning; multi-task learning; optical character recognition
UR - http://eudml.org/doc/271915
ER -

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