Evolutionary optimization (EMO) is one of the three fastest growing fields of research and applications among all computational intelligence topics. Evolutionary optimization algorithms use a population-based approach in which more than one solution participates in an iteration and evolves a new population of solutions in each iteration. Each individual in the population represents a potential solution to the problem being optimized. The population is expected to have high tendency to move towards better solution areas over iterations through cooperation and competition among themselves.

Over the past few years, evolutionary optimization algorithms, such as Genetic Algorithm s(GA), Differential Evolutionary Algorithms (DE), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), have successfully been used to deal with big data in several application domains. Some example applications include space planning, large scale scheduling, high-dimensional bioinformatics data, assembly line balancing and transmission, and genomics. However, with the amount of data growing constantly and exponentially, there are several challenges in evolutionary optimization for big data. For instances, the data processing tasks including data collection, data management, data analysis, data visualization, and real-world applications; and model strategies tasks including candidate generation strategies, search strategies, and optimization techniques. Therefore, to address those challenges, adaptive and efficient evolutionary optimization algorithms should be designed to handle massive data analytics problems.

With this perspective, this Special Issue aims to bring together cutting-edge research in all aspects of evolutionary optimization and big data, including experimental and theoretical research and real-world applications. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Big data analysis
• Evolutionary optimization for scheduling
• Evolutionary optimization for manufacturing optimization
• Hybrid evolutionary optimization for big data
• Parallel evolutionary optimization
• Many-objective big data
• Evolutionary multi-objective optimization using high performance computing
• Evolutionary machine learning and information extraction
• Genetic algorithms
• Particle swarm optimization
• Ant colony optimization