Optimization of dynamic bi-clustering based on improved genetic algorithm for microarray data

Due to the nature of microarray data, the analysis of genes/features for disease diagnosis is a challenging task. Generally, the data comes in the form of a 2D matrix, where the row represents the genes and the column indicates the various conditions. Bi-clustering is an emerging technique that can efficiently reveal patterns of genes. It can perform simultaneously with a subset of genes and conditions. Inspired by this, dynamic bi-clustering based on an improved genetic algorithm (GA) is proposed. The chromosomes are efficiently designed. In addition, the fitness function is derived by considering multiple conflicting objectives to measure the quality of a cluster. A novel mutation is designed by the correlation technique. The crossover and mutation rates are dynamically changed. The obtained outcomes of the proposed approach are compared with the various existing approaches, such as traditional GA, the dynamic dame parallel GA, the evolutionary local search algorithm, bi-phase evolutionary searching, and the evolutionary bi-clustering algorithm. Further, statistical tests such as the analysis of variance and Friedman test are executed to show the significance of the proposed model. A biological analysis is also performed.