Many complex applications in the real world can be represented and modeled as optimization problems, in which algorithms are required in order to locate the optimum. Automatic extraction of knowledge from massive data samples, for example, big data analytics (BDA), has emerged as a vital task in almost all scientific research fields. BDA problems are difficult to solve due to their discrete, large-scale, high-dimensional, and dynamic properties, while the problems with small data usually arise from insufficient data samples and incomplete information. Quality of data is another issue that should be considered. Such difficulties have led to search-based data analytics problems, where a data analysis task is modeled as a complex, dynamic, and computationally expensive optimization problem and then solved by using an iterative algorithm.

It is of great interest to investigate the role of evolutionary optimization (EC) techniques, including evolutionary algorithms and swarm intelligence algorithms, for optimization and learning involving big data analytics, in particular the ability of EC techniques to solve large-scale, dynamic, and sometimes multi-objective big data analytics problems. Intelligent optimization algorithms could be divided into two categories of approaches. In a model-driven (specific model-based methods) approach, the solved problem is formulated as a parametric model and the objective is to search for the optimal parameters that best fit the evidence. On the other hand, in a data-driven (generic model-based methods) approach, the features extracted are mapped from the solved problem to the landscape by learning from the data set.

This Special Issue aims to present the latest developments in EC techniques for big data analytics problems under uncertain environments, as well as to exchange new ideas and discuss the future direction of EC for data analytics. Original contributions that provide novel theories, frameworks, and solutions to challenging problems of big data analytics are very welcome. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Data-driven evolutionary computation methods for high-dimensional and many-objective optimization problems
• Integrative analytics of diverse, structured, and unstructured data
• Extracting new understanding from real-time, distributed, diverse, and large-scale data resources
• Scalable incremental learning and understanding of massive data
• Scalable learning techniques for massive data
• Data-driven optimization of complex systems
• Data analytics techniques for other critical application areas