Genetic optimization of asteroid families’ membership

Abstract

Asteroid families are groups of asteroids with a common origin, such as prior collisions or the parent body’s rotational fission. In proper [a, e, sin(i)] element domains, they are generally observed using the hierarchical clustering technique (HCMs), but the method may be ineffective in high-density regions, where it may be unable to separate near families. Previous works employed a different technique in which nine different machine learning classification algorithms were applied to the orbital distribution in proper elements of 21 known family constituents for the goal of new members’ identification. Each algorithm’s optimal hyper-parameters for every family were extensively investigated, which proved to be a time-consuming and repetitive procedure. Herein, we used a genetic algorithm-based tool to identify the most optimal machine learning algorithm for the same studied asteroid families as an alternative to the originally utilized parameter search mode. When compared to the same evaluative metrics utilized in the previous machine learning application study, the precision values of the new genetic machine learning algorithms have been consistently comparable, demonstrating that this alternative technique can be satisfactorily efficient and fast.