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

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@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

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