Adaptive bound reduced-form genetic algorithms for B-spline neural network training

Wei Yen Wang*, Chin Wang Tao, Chen Guan Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


In this paper, an adaptive bound reduced-form genetic algorithm (ABRGA) to tune the control points of B-spline neural networks is proposed. It is developed not only to search for the optimal control points but also to adaptively tune the bounds of the control points of the B-spline neural networks by enlarging the search space of the control points. To improve the searching speed of the reduced-form genetic algorithm (RGA), the ABRGA is derived, in which better bounds of control points of B-spline neural networks are determined and paralleled with the optimal control points searched. It is shown that better efficiency is obtained if the bounds of control points are adjusted properly for the RGA-based B-spline neural networks.

Original languageEnglish
Pages (from-to)2479-2488
Number of pages10
JournalIEICE Transactions on Information and Systems
Issue number11
Publication statusPublished - 2004 Nov
Externally publishedYes


  • Adaptive bounds
  • B-spline neural networks
  • Nonlinear function approximation
  • Reduced-form genetic algorithms

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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