Multiple sequence alignment is one of the important research topics of bioinformatics. The objective is to maximize the similarities between them by adding and shuffling gaps. We propose a hybrid algorithm based on genetic (GAs) and 2-optimal algorithms. We are using permutation coding corresponding to represent the solution, and we are studying scoring function for multiple alignments, that is used as fitness function. Our GA is implemented with two selections strategies and different crossovers. The probability of crossover and mutation are set as one. Performance and comparison of the proposed GA is analyzed and the obtained solution qualities are reported.

In modern textile industry, Tissue on line Automatic Inspection (TAI) is becoming an attractive alternative to human vision inspection (HVI). HVI needs a high level of human attention leading to a low level of performance in term of tissue inspection.Based on the advances in image processing and pattern recognition, TAI can potentially provide an objective and reliable evaluation on the fabric production quality. Most TAI systems claim to be able to detect the presence of defects in fabric products, and precisely locate the defects position. The motivation behind the fabric defect identification is to enable an on-line quality control of the weaving process.In this paper, a method based on texture analysis approach for identifying fabric defects in each image is proposed. Neural Networks and Mutual information are employed to characterize the texture property of a tissue image. A feature extractor is designed based on Mutual Information computation in conjunction with a classifier to minimize the error rate in defect classification.

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