  Outlier Detection is a critical and cardinal research task due its array of
applications in variety of domains ranging from data mining, clustering,
statistical analysis, fraud detection, network intrusion detection and
diagnosis of diseases etc. Over the last few decades, distance-based outlier
detection algorithms have gained significant reputation as a viable alternative
to the more traditional statistical approaches due to their scalable,
non-parametric and simple implementation. In this paper, we present a modified
onion peeling (Convex hull) genetic algorithm to detect outliers in a Gaussian
2-D point data set. We present three different scenarios of outlier detection
using a) Euclidean Distance Metric b) Standardized Euclidean Distance Metric
and c) Mahalanobis Distance Metric. Finally, we analyze the performance and
evaluate the results.
