In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features for use in model construction. The central assumption when using a feature selection technique is that the data contains many redundant or irrelevant features. Redundant features are those which provide no more information than the currently selected features, and irrelevant features provide no useful information in any context. Thus finding a “compact” subset of non-redundant and relevant features is an important machine learning problem.We are trying to improvise the Feature Selection Algorithm by giving more relevant results in the copacetic time period using pairwise symbiotic evolution.