MGIGA: An Iterative Merge Based Genetic Algorithm for Motif Discovery in Genomic Sequences

Motif discovery is an important problem in bio-informatics that involves the search for approximate matches. Various algorithms have been proposed, including exhaustive searches as well as heuristic searches that involve searching only a subset of all the possible solutions. One such often employed...

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Bibliographic Details
Main Authors: Srinivasa, K.G., Pendyala, Srichand, Bhatt, Anuj, Venugopal, K.R., Patnaik, L.M.
Format: Conference Proceeding
Language:English
Subjects:
Bioinformatics
Computational intelligence
Data engineering
Data mining
Educational institutions
Genetic algorithms
Genomics
Laboratories
Merging
Sequences
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Summary:Motif discovery is an important problem in bio-informatics that involves the search for approximate matches. Various algorithms have been proposed, including exhaustive searches as well as heuristic searches that involve searching only a subset of all the possible solutions. One such often employed method is the genetic algorithm. A genetic algorithm based approach is employed in MDGA, using a single population. In this paper, we present an iterative merge based genetic algorithms for motif discovery in genomic sequences and the results are compared with that of standard methods on well known datasets.
DOI:10.1109/ICCIMA.2007.315