ABSTRACT
In the technology-based world the enormous utilization of microarray technology plays a major role in biomarker identification. But still it is an expensive task to accomplish due to the curse of the dimensionality issue. To achieve this objective a different assortment of meta-heuristic approaches are utilized for simple and precision-based classification. This chapter deals with an improvised binary shuffled frog-leaping algorithm and a meta-heuristic methodology for biomarker gene determination. The authors have abstracted various combinations of datasets with a minimum of 25 numbers and using various well-known classifiers, for example, KNN, SVM, and ANN. The result is compared with various existing meta-heuristic approaches to analyze the effectiveness of the proposed one.
