Automatic extracting of knowledge from massive data samples, i.e., big data analytics (BDA), has emerged as a vital task in almost all scientific research fields. The BDA problems are rather difficult to solve due to their large-scale, high-dimensional, and dynamic properties, while the problems with small data are usually hard to handle due to insufficient data samples and incomplete information. Such difficulties lead to the search-based data analytics problem, where a data analysis task is modeled as a complex, dynamic, and computationally expensive optimization problem and then solved by using an iterative algorithm. In this paper, we intend to present an extensive and in-depth discussion on the utilizing of evolutionary computation (EC) based optimization methods [including evolutionary algorithms (EAs) and swarm intelligence (SI)] for solving search-based data analysis problems. Then, as an example for illustration, we provide a comprehensive review of the applications of state-of-the-art EC methods for different types of data mining problems in bioinformatics. Here, the detailed analysis and discussion are conducted on three types of data samples, which include sequences data, network data, and image data. Finally, we survey the challenges faced by EC methods and the trend for future directions. Based on the applications of EC methods for search-based data analysis problems involving inexact and uncertain information, the insights of data analytics are able to understand better, and more efficient algorithms could be designed to solve real-world complex BDA problems.

中文翻译：

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用于解决基于搜索的数据分析问题的进化计算

从海量数据样本中自动提取知识，即大数据分析（BDA），已成为几乎所有科学研究领域的一项重要任务。BDA问题由于其规模大、维数高、动态性强，比较难解决，而小数据问题往往由于数据样本不足、信息不完整而难以处理。这些困难导致了基于搜索的数据分析问题，其中数据分析任务被建模为复杂、动态且计算成本高的优化问题，然后使用迭代算法解决。在本文中，我们打算对利用基于进化计算 (EC) 的优化方法 [包括进化算法 (EA) 和群智能 (SI)] 来解决基于搜索的数据分析问题进行广泛而深入的讨论。然后，作为说明的示例，我们全面回顾了最先进的 EC 方法在生物信息学中不同类型的数据挖掘问题中的应用。在这里，对三类数据样本进行了详细的分析和讨论，包括序列数据、网络数据和图像数据。最后，我们调查了 EC 方法面临的挑战以及未来方向的趋势。基于 EC 方法在涉及不精确和不确定信息的基于搜索的数据分析问题中的应用，