Function approximations by superimposing genetic programming trees:with applications to engineering problems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{YunSeogYeun:2001:IS,
  author =       "Yun Seog Yeun and Jun Chen Suh and Young-Soon Yang",
  title =        "Function approximations by superimposing genetic
                 programming trees:with applications to engineering
                 problems",
  journal =      "Information Sciences",
  year =         "2000",
  volume =       "122",
  number =       "2-4",
  pages =        "259--280",
  email =        "yeonyun@road.daejin.ac.kr",
  keywords =     "genetic algorithms, genetic programming, Function
                 approximation, Linear associative memory, Group of
                 additive genetic programming tree",
  URL =          "http://members.kr.inter.net/yyshuj/paper/gagpt.zip",
  URL =          "http://www.elsevier.com/gej-ng/10/23/143/56/27/34/article.pdf",
  DOI =          "doi:10.1016/S0020-0255(99)00121-8",
  abstract =     "This paper concerns fundamental issues regarding
                 genetic programming (GP) as a tool for real-valued
                 function approximations. Standard GP suffers from the
                 lack of estimation techniques for numerical parameters
                 of a functional tree. Unlike other research activities,
                 where non-linear optimization techniques are employed,
                 we adopt the use of a linear associative memory for the
                 estimation of these parameters under the GP algorithm.
                 Instead of dealing with a large associative matrix, we
                 present the method of building several associative
                 matrixes in small size, each of which is responsible
                 for determining the value for different small portions
                 of the whole parameter. This approach can significantly
                 reduce computational cost, and a reasonably accurate
                 value for parameters can be obtained. Due to the fact
                 that the GP algorithm is likely to fall into a local
                 minimum, the GP algorithm often fails to generate the
                 functional tree with the desired accuracy. This
                 motivates us to devise a group of additive genetic
                 programming trees(GAGPT) which consists of a primary
                 tree and a set of auxiliary trees. The output of the
                 GAGPT is the summation of outputs of the primary tree
                 and all auxiliary trees. The addition of auxiliary
                 trees makes it possible to improve both the learning
                 and generalization capability of the GAGPT, since the
                 auxiliary tree evolves toward refining the quality of
                 the GAGPT by optimizing its fitness function. The
                 effectiveness of our approach is verified by applying
                 the GAGPT to the estimation of the principal dimensions
                 of a bulk cargo ship and engine torque of a passenger
                 car.",
  notes =        "Information Sciences
                 http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt",
}

Genetic Programming entries for Yun Seog Yeun Jun Chen Suh Young-Soon Yang

Citations