Cross-task code reuse in genetic programming applied to visual learning
Created by W.Langdon from
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- @Article{journals/amcs/JaskowskiKW14,
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title = "Cross-task code reuse in genetic programming applied
to visual learning",
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author = "Wojciech Jaskowski and Krzysztof Krawiec and
Bartosz Wieloch",
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journal = "Applied Mathematics and Computer Science",
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year = "2014",
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number = "1",
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volume = "24",
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pages = "183--197",
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keywords = "genetic algorithms, genetic programming, code reuse,
knowledge sharing, visual learning, multi-task
learning, optical character recognition",
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bibdate = "2014-05-05",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/amcs/amcs24.html#JaskowskiKW14",
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URL = "http://dx.doi.org/10.2478/amcs-2014-0014",
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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 recognise 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 recognised objects exhibit visual similarity",
- }
Genetic Programming entries for
Wojciech Jaskowski
Krzysztof Krawiec
Bartosz Wieloch
Citations