The Meta-Heuristic Binary Shuffled Frog Leaping and Genetic Algorithms in Selecting Efficient Significant Features

Selecting the most suitable features among a collection of features to achieve accuracy, sensitivity and efficiency is considered as a big challenge in pattern recognition systems. In this study, the two binary genetic and the binary shuffled frog leaping evolutionary algorithms are evaluated with respect to efficient feature selection in a medical detecting system. The results point to the effectiveness of selection of the most suitable features through memetic Meta heuristic binary frog leaping in increasing the accuracy, sensitivity in detection and time saving in the Classification process against the genetic algorithm.