  What is habitability? Can we quantify it? What do we mean under the term
habitable or potentially habitable planet? With estimates of the number of
planets in our Galaxy alone running into billions, possibly a number greater
than the number of stars, it is high time to start characterizing them, sorting
them into classes/types just like stars, to better understand their formation
paths, their properties and, ultimately, their ability to beget or sustain
life. After all, we do have life thriving on one of these billions of planets,
why not on others? Which planets are better suited for life and which ones are
definitely not worth spending expensive telescope time on? We need to find sort
of quick assessment score, a metric, using which we can make a list of
promising planets and dedicate our efforts to them. Exoplanetary habitability
is a transdisciplinary subject integrating astrophysics, astrobiology,
planetary science, even terrestrial environmental sciences. We review the
existing metrics of habitability and the new classification schemes of
extrasolar planets and provide an exposition of the use of computational
intelligence techniques to evaluate habitability scores and to automate the
process of classification of exoplanets. We examine how solving convex
optimization techniques, as in computing new metrics such as CDHS and CEESA,
cross-validates ML-based classification of exoplanets. Despite the recent
criticism of exoplanetary habitability ranking, this field has to continue and
evolve to use all available machinery of astroinformatics, artificial
intelligence and machine learning. It might actually develop into a sort of
same scale as stellar types in astronomy, to be used as a quick tool of
screening exoplanets in important characteristics in search for potentially
habitable planets for detailed follow-up targets.
