A lately created metaheuristic algorithm called Child Drawing Development Optimization (CDDO) has proven to be effective in a number of benchmark tests. A Binary Child Drawing Development Optimization (BCDDO) is suggested for choosing the wrapper features in this study. To achieve the best classification accuracy, a subset of crucial features is selected using the suggested BCDDO. The proposed feature selection technique's efficiency and effectiveness are assessed using the Harris Hawk, Grey Wolf, Salp, and Whale optimization algorithms. The suggested approach has significantly outperformed the previously discussed techniques in the area of feature selection to increase classification accuracy. Moderate COVID, breast cancer, and big COVID are the three datasets utilized in this study. The classification accuracy for each of the three datasets was (98.75, 98.83%, and 99.36) accordingly.

翻译：最新提出的元启发式算法——儿童绘画发展优化（Child Drawing Development Optimization, CDDO）已在多项基准测试中展现出有效性。本研究提出了一种二元儿童绘画发展优化算法（Binary Child Drawing Development Optimization, BCDDO），用于封装特征选择。该算法通过选择关键特征子集，实现最优分类精度。采用哈里斯鹰、灰狼、海鞘和鲸鱼四种优化算法评估所提特征选择方法的效率与效能。实验表明，在提升分类精度的特征选择领域，本方法显著优于前述技术。研究采用中度新冠、乳腺癌及重度新冠三组数据集，对应分类精度分别为98.75%、98.83%和99.36%。