  A profound shift in the study of cosmology came with the discovery of
thousands of exoplanets and the possibility of the existence of billions of
them in our Galaxy. The biggest goal in these searches is whether there are
other life-harbouring planets. However, the question which of these detected
planets are habitable, potentially-habitable, or maybe even inhabited, is still
not answered. Some potentially habitable exoplanets have been hypothesized, but
since Earth is the only known habitable planet, measures of habitability are
necessarily determined with Earth as the reference. Several recent works
introduced new habitability metrics based on optimization methods.
Classification of potentially habitable exoplanets using supervised learning is
another emerging area of study. However, both modeling and supervised learning
approaches suffer from drawbacks. We propose an anomaly detection method, the
Multi-Stage Memetic Algorithm (MSMA), to detect anomalies and extend it to an
unsupervised clustering algorithm MSMVMCA to use it to detect potentially
habitable exoplanets as anomalies. The algorithm is based on the postulate that
Earth is an anomaly, with the possibility of existence of few other anomalies
among thousands of data points. We describe an MSMA-based clustering approach
with a novel distance function to detect habitable candidates as anomalies
(including Earth). The results are cross-matched with the habitable exoplanet
catalog (PHL-HEC) of the Planetary Habitability Laboratory (PHL) with both
optimistic and conservative lists of potentially habitable exoplanets.
