Learning Weights in Genetic Programs Using Gradient Descent for Object Recognition
Created by W.Langdon from
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- @InProceedings{zhang:evows05,
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author = "Mengjie Zhang and Will Smart",
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title = "Learning Weights in Genetic Programs Using Gradient
Descent for Object Recognition",
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booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2005: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
{EvoIASP}, {EvoMUSART}, {EvoSTOC}",
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year = "2005",
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month = "30 " # mar # "-1 " # apr,
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editor = "Franz Rothlauf and Juergen Branke and
Stefano Cagnoni and David W. Corne and Rolf Drechsler and
Yaochu Jin and Penousal Machado and Elena Marchiori and
Juan Romero and George D. Smith and Giovanni Squillero",
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series = "LNCS",
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volume = "3449",
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publisher = "Springer Verlag",
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address = "Lausanne, Switzerland",
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publisher_address = "Berlin",
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pages = "417--427",
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keywords = "genetic algorithms, genetic programming, evolutionary
computation",
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ISBN = "3-540-25396-3",
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ISSN = "0302-9743",
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DOI = "doi:10.1007/b106856",
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abstract = "the use of gradient descent search in tree based
genetic programming for object recognition problems. A
weight parameter is introduced to each link between two
nodes in a program tree. The weight is defined as a
floating point number and determines the degree of
contribution of the sub-program tree under the link
with the weight. Changing a weight corresponds to
changing the effect of the sub-program tree. The weight
changes are learnt by gradient descent search at a
particular generation. The programs are evolved and
learned by both the genetic beam search and the
gradient descent search. This approach is examined and
compared with the basic genetic programming approach
without gradient descent on three object classification
problems of varying difficulty. The results suggest
that the new approach works well on these problems.",
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notes = "EvoWorkshops2005",
- }
Genetic Programming entries for
Mengjie Zhang
Will Smart
Citations