In the past 30 years, scientists have searched nature, including animals and
insects, and biology in order to discover, understand, and model solutions for
solving large-scale science challenges. The study of bionics reveals that how
the biological structures, functions found in nature have improved our modern
technologies. In this study, we present our discovery of evolutionary and
nature-inspired algorithms applications in Data Science and Data Analytics in
three main topics of pre-processing, supervised algorithms, and unsupervised
algorithms. Among all applications, in this study, we aim to investigate four
optimization algorithms that have been performed using the evolutionary and
nature-inspired algorithms within data science and analytics. Feature selection
optimization in pre-processing section, Hyper-parameter tuning optimization,
and knowledge discovery optimization in supervised algorithms, and clustering
optimization in the unsupervised algorithms.

研究发现，在自然界的生物结构和功能中寻找灵感，运用进化算法等自然启发算法最终可以提高数据科学和数据分析的表现，其中特别关注了四种算法的优化应用 —— 特征选择、超参数调整、知识发现和聚类优化。