A neuro-genetic framework for multi-classifier design: An application to promoter recognition in DNA sequences

Romesh Ranawana, Vasile Palade

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

This chapter presents a novel methodology for the customization of neural network based multi-classifiers used for the recognition of promoter regions in genomic DNA. We present a framework that utilizes genetic algorithms (GA's) for the determination of optimal neural network parameters for better promoter recognition. The framework also presents a GA based method for the combination of the designed neural networks into the multi-classifier system.

Original languageEnglish
Title of host publicationAdvances in Evolutionary Computing for System Design
EditorsLakhmi C. Jain, Vasile Palade, Dipti Srinivasan
Place of PublicationBerlin
PublisherSpringer
Pages71-94
Number of pages24
Volume66
ISBN (Electronic)978-3-540-72377-6
ISBN (Print)3540723765, 978-3-540-72376-9
DOIs
Publication statusPublished - 2007
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume66
ISSN (Print)1860949X

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Ranawana, R., & Palade, V. (2007). A neuro-genetic framework for multi-classifier design: An application to promoter recognition in DNA sequences. In L. C. Jain, V. Palade, & D. Srinivasan (Eds.), Advances in Evolutionary Computing for System Design (Vol. 66, pp. 71-94). (Studies in Computational Intelligence; Vol. 66). Berlin: Springer. https://doi.org/10.1007/978-3-540-72377-6_4