Evolving Weights in Genetic Programs Using Gradient Descent

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@TechReport{vuw-CS-TR-04-11,

• author = "Will Smart and Mengjie Zhang",
• title = "Evolving Weights in Genetic Programs Using Gradient Descent",
• institution = "Computer Science, Victoria University of Wellington",
• year = "2004",
• number = "CS-TR-04-11",
• address = "New Zealand",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-04/CS-TR-04-11.pdf",
• URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-04-11.abs.html",
• abstract = "gradient descent search in tree based genetic programming for object recognition problems. To learn better partial programs, a weight parameter is introduced in each link between every two nodes in a program tree, so that a change of a weight corresponds to a change of the effect of the sub-program tree. Inside a particular generation, weight changes are learnt locally by gradient descent search, but the whole evolution process is still carried out across different generations globally by the genetic beam search. This approach is examined and compared with the basic genetic programming approach without gradient descent on three object classification problems of various difficulty. The results suggest that the new approach outperforms the basic approach on all problems.",

}

Genetic Programming entries for Will Smart Mengjie Zhang

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