Genetic Bi-objective Optimization Approach to Habitability Score
This work addresses the need for astronomers to classify exoplanet habitability, but it is incremental as it applies an existing optimization method to a known function without introducing new paradigms.
The paper tackles the problem of classifying exoplanet habitability by using Genetic Algorithms to optimize the Cobb-Douglas Habitability Score, achieving optimal values for a set of promising exoplanets through bi-objective and single-objective formulations.
The search for life outside the Solar System is an endeavour of astronomers all around the world. With hundreds of exoplanets being discovered due to advances in astronomy, there is a need to classify the habitability of these exoplanets. This is typically done using various metrics such as the Earth Similarity Index or the Planetary Habitability Index. In this paper, Genetic Algorithms are used to evaluate the best possible habitability scores using the Cobb-Douglas Habitability Score. Genetic Algorithm is a classic evolutionary algorithm used for solving optimization problems. It is based on Darwin's theory of evolution, "Survival of the fittest". The working of the algorithm is established through comparison with various benchmark functions and extended its functionality to Multi-Objective optimization. The Cobb-Douglas Habitability Function is formulated as a bi-objective as well as a single objective optimization problem to find the optimal values to maximize the Cobb-Douglas Habitability Score for a set of promising exoplanets.