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Optimization of Neuro-Fuzzy System Using Genetic Algorithm for Chromosome Classification

Journal of ICT Research and Applications (JICTRA) · Vol. 1 No. 1 (2013) · pp. 56–69

DOI
10.5614/itbj.ict.2007.1.1.5
ISSN
2338-5499, 2337-5787
Published
2013-09-13
Publisher
DRI - ITB
Language
eng

Abstract

Neuro-fuzzy system has been shown to provide a good performance on chromosome classification but does not offer a simple method to obtain the accurate parameter values required to yield the best recognition rate. This paper presents a neuro-fuzzy system where its parameters can be automatically adjusted using genetic algorithms. The approach combines the advantages of fuzzy logic theory, neural networks, and genetic algorithms. The structure consists of a four layer feed-forward neural network that uses a GBell membership function as the output function. The proposed methodology has been applied and tested on banded chromosome classification from the Copenhagen Chromosome Database. Simulation result showed that the proposed neuro-fuzzy system optimized by genetic algorithms offers advantages in setting the parameter values, improves the recognition rate significantly and decreases the training/testing time which makes genetic neuro-fuzzy system suitable for chromosome classification.

Collaboration network of the authors

10 authors · 16 co-authorship links · 5 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Bandung Institute of Technology
  • Politeknik Negeri Malang
  • University of Brawijaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Herman TolleUniversity of Brawijaya1
Mokhamad Hadi WijayaUniversity of Brawijaya1
Nailul MunaPoliteknik Negeri Malang1
Barra RafiAffiliation unknown1
Arwin SumariAffiliation unknown1
Agil EvanAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Optimization of Neuro-Fuzzy System Using Genetic Algorithm for Chromosome Classification</dc:title>
	<dc:creator>Sarosa, M.</dc:creator>
	<dc:creator>Ahmad, A. S.</dc:creator>
	<dc:creator>Riyanto, B.</dc:creator>
	<dc:creator>Noer, A. S.</dc:creator>
	<dc:description xml:lang="en-US">Neuro-fuzzy system has been shown to provide a good performance on chromosome classification but does not offer a simple method to obtain the accurate parameter values required to yield the best recognition rate. This paper presents a neuro-fuzzy system where its parameters can be automatically adjusted using genetic algorithms. The approach combines the advantages of fuzzy logic theory, neural networks, and genetic algorithms. The structure consists of a four layer feed-forward neural network that uses a GBell membership function as the output function. The proposed methodology has been applied and tested on banded chromosome classification from the Copenhagen Chromosome Database. Simulation result showed that the proposed neuro-fuzzy system optimized by genetic algorithms offers advantages in setting the parameter values, improves the recognition rate significantly and decreases the training/testing time which makes genetic neuro-fuzzy system suitable for chromosome classification.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2013-09-13</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/166</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.2007.1.1.5</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 1 No. 1 (2007); 56-69</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/166/160</dc:relation>
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