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—This paper provides an overview of the application of evolutionary algorithms in certain bioinformatics tasks. Different tasks such as gene sequence analysis, gene mapping, deoxyri-bonucleic acid (DNA) fragment assembly, gene finding, microar-ray analysis, gene regulatory network analysis, phylogenetic trees, structure prediction and analysis of DNA,(More)
This paper deals with some new operators of genetic algorithms and demonstrates their effectiveness to the traveling salesman problem (TSP) and microarray gene ordering. The new operators developed are nearest fragment operator based on the concept of nearest neighbor heuristic, and a modified version of order crossover operator. While these result in(More)
This paper describes an application of genetic algorithm to the traveling salesman problem. New knowledge based multiple inversion operator and a neighborhood swapping operator are proposed. Experimental results on different benchmark data sets have been found to provide superior results as compared to some other existing methods.
Keywords: Fuzzy rough sets Rule based layered network Fuzzy reflexive relation Unsupervised learning Cluster validity index Natural computing Microarray Gene-function a b s t r a c t A fuzzy rough granular self-organizing map (FRGSOM) involving a 3-dimensional linguistic vector and connection weights, defined in an unsupervised manner, is proposed for(More)
One of the important goals of most biological investigations is to classify and organize the experimental findings so that they are readily useful for deriving generalized rules. Although there is a huge amount of information on RNA structures in PDB, there are redundant files, ambiguous synthetic sequences etc. Moreover, a systematic hierarchical(More)
This investigation deals with a new distance measure for genes using their microarray expressions and a new algorithm for fast gene ordering without clustering. This distance measure is called "Maxrange distance," where the distance between two genes corresponding to a particular type of experiment is computed using a normalization factor, which is(More)
This paper deals with some new operators of genetic algorithms for solving the traveling salesman problem (TSP). These include a new operator called, " nearest fragment operator " based on the concept of nearest neighbor heuristic, and a modified version of order crossover operator. Superiority of these operators has been established on different benchmark(More)
A granular neural network for identifying salient features of data, based on the concepts of fuzzy set and a newly defined fuzzy rough set, is proposed. The formation of the network mainly involves an input vector, initial connection weights and a target value. Each feature of the data is normalized between 0 and 1 and used to develop granulation structures(More)
A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering and ordering the genes using gene expression data into homogeneous groups was shown to be useful in functional annotation, tissue classification, regulatory motif identification, and other applications.(More)
MOTIVATION One of the important goals of biological investigation is to predict the function of unclassified gene. Although there is a rich literature on multi data source integration for gene function prediction, there is hardly any similar work in the framework of data source weighting using functional annotations of classified genes. In this(More)