Child Drawing Development Optimization is a recently developed metaheuristic algorithm that has been demonstrated to perform well on multiple benchmark tests. In this paper, a binary Child Drawing Development Optimization (BCDDO) is proposed for wrapper feature selection. The proposed BCDDO is utilized to choose a subset of important features to reach the highest classification accuracy. Harris Hawk optimization, salp swarm algorithm, gray wolf optimization, and whale optimization algorithm are utilized to evaluate the effectiveness and efficiency of the suggested feature selection method. In the field of feature selection to improve classification accuracy, the proposed method has gained a considerable classification accuracy advantage over previously mentioned methods. Four datasets are used in this research work; breast cancer, moderate COVID, big COVID, and Iris using XGBoost classifier and the classification accuracies were (98.83%, 98.75%, 99.36%, and 96%), respectively, for the four mentioned datasets.

儿童绘画开发优化是一种最近开发的元启发式算法，已被证明在多个基准测试中表现良好。在本文中，提出了一种用于包装器特征选择的二进制儿童绘图开发优化（BCDDO）。所提出的 BCDDO 用于选择重要特征的子集以达到最高的分类精度。利用 Harris Hawk 优化、樽海鞘群算法、灰狼优化和鲸鱼优化算法来评估所提出的特征选择方法的有效性和效率。在提高分类精度的特征选择领域，所提出的方法比之前提到的方法获得了相当大的分类精度优势。这项研究工作使用了四个数据集；使用 XGBoost 分类器对乳腺癌、中度新冠病毒、大新冠病毒和虹膜进行分类，上述四个数据集的分类准确率分别为（98.83%、98.75%、99.36% 和 96%）。