论文标题
遗传双目标优化方法的可居住性评分
Genetic Bi-objective Optimization Approach to Habitability Score
论文作者
论文摘要
在太阳系之外寻找生活是世界各地天文学家的努力。由于天文学的进步，发现了数百个系外行星，因此有必要对这些系外行星的可居住性进行分类。这通常是使用各种指标（例如地球相似性指数或行星宜居性指数）完成的。在本文中，遗传算法用于使用Cobb-Douglas的可居住性评分来评估最佳的可居住性评分。遗传算法是一种用于解决优化问题的经典进化算法。它基于达尔文的进化论，“适得生意的生存”。该算法的工作是通过与各种基准功能进行比较建立的，并将其功能扩展到多目标优化。 COBB-DOUGLAS的可居住性函数被配制为双人目标以及单个客观优化问题，以找到最佳值，以最大程度地提高一组有希望的系外行星的Cobb-Douglas可居住性评分。
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.
