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

DOI10.1016/S0020-0255(99)00121-8zbMath1147.68663OpenAlexW2011592173MaRDI QIDQ1854043

J.-C. Suh, Young Soon Yang, Yun Seog Yeun

Publication date: 23 January 2003

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0020-0255(99)00121-8

zbMATH Keywords

genetic programmingfunction approximationlinear associative memorygroup of additive genetic programming trees

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Polynomial genetic programming for response surface modeling. I. A methodology ⋮ Smooth fitting with a method for determining the regularization parameter under the genetic programming algorithm

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• An adaptive conjugate gradient learning algorithm for efficient training of neural networks
• Weighted linear associative memory approach to nonlinear parameter estimation

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