Abstract
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. The working of the algorithm is established through comparison with various benchmark functions and its functionality is extended 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.
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Acknowledgment
We thank our teachers who gave us this opportunity to work on something innovative and provided us a rich learning experience in the process. We thank Dr. Snehanshu Saha for assistance with this endeavour and guiding us in this project. And finally, we would also like to show our gratitude to the PES University for allowing us to go beyond our coursework and work on a project with practical applications.
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Krishna, S., Pentapati, N. (2020). Genetic Bi-objective Optimization Approach to Habitability Score. In: Saha, S., Nagaraj, N., Tripathi, S. (eds) Modeling, Machine Learning and Astronomy. MMLA 2019. Communications in Computer and Information Science, vol 1290. Springer, Singapore. https://doi.org/10.1007/978-981-33-6463-9_12
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DOI: https://doi.org/10.1007/978-981-33-6463-9_12
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