@inproceedings{Fukumi1995,
  abstract = {This paper presents a method of using the genetic algorithm (GA) with
	partial fitness (PF) to design a neural network for coin recognition.
	The method divides a chromosome in the GA into several parts, the
	PFs of which are evaluated for GA operations. Each part independently
	performs selection and crossover operations in the GA. Such a technique
	improves performance in learning of the GA. This paper applies the
	method to a rotated coin recognition problem to examine its effectiveness.
	The coin recognition system described consists of a preprocessor
	with Fourier transform and a multilayered network. The method is
	utilized to reduce the number of input signals, Fourier spectra,
	of the multilayered network. It is shown that the method is better
	than the conventional GA on convergence in learning and makes a smaller
	size network},
  added-at = {2011-03-27T19:35:34.000+0200},
  affiliation = {University of Tokushima, Faculty of Engineering, Department of Information
	Science and Intelligent Systems},
  author = {Fukumi, Minoru and Omatu, Sigeru and Nishikawa, Yoshikazu},
  biburl = {https://www.bibsonomy.org/bibtex/2b3fb295df741fb9bf9424735d707e983/cocus},
  booktitle = {Proceedings of the IEEE International joint Conference on Neural
	Networks 1995, IJCNN'95},
  booktitleaddon = {Jun 27 -- Jul 2, 1995},
  doi = {10.1109/ICNN.1995.488900},
  file = {:./fukumi1995_00488900.pdf:PDF},
  interhash = {8bc4448889bcc2ee1fa4c697ea8e504a},
  intrahash = {b3fb295df741fb9bf9424735d707e983},
  keywords = {(artificial Fourier algorithm, algorithms, chromosome, coin convergence convergence, crossover feedforward fitness, genetic intelligence), learning learning, methods, nets, network, neural numerical object of operation operation, partial recognition recognitionFourier selection spectra, system, transform transform,},
  location = {#ieeeaddr#},
  month = nov,
  owner = {CK},
  pages = {1834--1838},
  publisher = {{IEEE} Computer Society},
  timestamp = {2011-03-27T19:35:39.000+0200},
  title = {Designing a neural network by a genetic algorithm with partial fitness},
  titleaddon = {\noop{}},
  url = {http://ieeexplore.ieee.org/iel2/3505/10434/00488900.pdf?tp=&arnumber=488900&isnumber=10434},
  urldate = {25-1-2008},
  venue = {Beijing, China},
  volume = 4,
  volumes = {6},
  xcrossref = {CK:conf/ijcnn/1995},
  year = 1995
}