The heavenly bodies are objects that swim in the outer space. The classification of these objects is a challenging task for astronomers. This article presents a novel methodology that enables an efficient and accurate classification of cosmic objects (3 classes) based on evolutionary optimization of classifiers. This research collected the data from Sloan Digital Sky Survey database. In this work, we are proposing to develop a novel machine learning model to classify stellar spectra of stars, quasars and galaxies. First, the input data are normalized and then subjected to principal component analysis to reduce the dimensionality. Then, the genetic algorithm is implemented on the data which helps to find the optimal parameters for the classifiers. We have used 21 classifiers to develop an accurate and robust classification with fivefold cross-validation strategy. Our developed model has achieved an improvement in the accuracy using nineteen out of twenty-one models. We have obtained the highest classification accuracy of 99.16%, precision of 98.78%, recall of 98.08% and F1-score of 98.32% using evolutionary system based on voting classifier. The developed machine learning prototype can help the astronomers to make accurate classification of heavenly bodies in the sky. Proposed evolutionary system can be used in other areas where accurate classification of many classes is required.

天体是在外太空中游动的物体。对天文学家来说，这些物体的分类是一项艰巨的任务。本文介绍了一种新颖的方法，该方法可基于分类器的进化优化对宇宙对象（3个类）进行有效且准确的分类。这项研究从Sloan Digital Sky Survey数据库中收集了数据。在这项工作中，我们提议开发一种新颖的机器学习模型来对恒星，类星体和星系的恒星光谱进行分类。首先，对输入数据进行规格化，然后进行主成分分析以降低维数。然后，对数据实施遗传算法，这有助于找到分类器的最佳参数。我们已经使用21个分类器开发了具有五重交叉验证策略的准确而强大的分类。我们开发的模型使用21种模型中的19种提高了准确性。使用基于投票分类器的进化系统，我们获得了最高的分类准确率99.16％，准确度98.78％，召回率98.08％和F1得分98.32％。开发的机器学习原型可以帮助天文学家对天空中的天体进行准确分类。拟议的进化系统可用于需要对许多类别进行准确分类的其他领域。使用基于投票分类器的进化系统，召回率为98.08％，F1得分为98.32％。开发的机器学习原型可以帮助天文学家对天空中的天体进行准确分类。拟议的进化系统可用于需要对许多类别进行准确分类的其他领域。使用基于投票分类器的进化系统，召回率为98.08％，F1得分为98.32％。开发的机器学习原型可以帮助天文学家对天空中的天体进行准确分类。拟议的进化系统可用于需要对许多类别进行准确分类的其他领域。