Soft computing techniques have been recently exploited as a promising tool for achieving high performance in pattem recognition. This paper presents a hybrid method which combines neural network classifiers by genetic algorithm. Genetic algorithm gives us an effective vehicle to determine the optima
Patterning by genetic networks
β Scribed by S. Genieys; S. Vakulenko
- Book ID
- 102514029
- Publisher
- John Wiley and Sons
- Year
- 2005
- Tongue
- English
- Weight
- 150 KB
- Volume
- 29
- Category
- Article
- ISSN
- 0170-4214
- DOI
- 10.1002/mma.670
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β¦ Synopsis
Abstract
We consider here the morphogenesis (pattern formation) problem for some genetic network models. First, we show that any given spatioβtemporal pattern can be generated by a genetic network involving a sufficiently large number of genes. Moreover, patterning process can be performed by an effective algorithm. We also show that Turing's or Meinhardt's type reactionβdiffusion models can be approximated by genetic networks.
These results exploit the fundamental fact that the genes form functional units and are organized in blocks. Due to this modular organization, the genes always are capable to construct any new patterns and even any time sequences of new patterns from old patterns. Computer simulations illustrate some analytical results. Copyright Β© 2005 John Wiley & Sons, Ltd.
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