  A large number of engineering, science and computational problems have yet to
be solved in a computationally efficient way. One of the emerging challenges is
how evolving technologies grow towards autonomy and intelligent decision
making. This leads to collection of large amounts of data from various sensing
and measurement technologies, e.g., cameras, smart phones, health sensors,
smart electricity meters, and environment sensors. Hence, it is imperative to
develop efficient algorithms for generation, analysis, classification, and
illustration of data. Meanwhile, data is structured purposefully through
different representations, such as large-scale networks and graphs. We focus on
data science as a crucial area, specifically focusing on a curse of
dimensionality (CoD) which is due to the large amount of
generated/sensed/collected data. This motivates researchers to think about
optimization and to apply nature-inspired algorithms, such as evolutionary
algorithms (EAs) to solve optimization problems. Although these algorithms look
un-deterministic, they are robust enough to reach an optimal solution.
Researchers do not adopt evolutionary algorithms unless they face a problem
which is suffering from placement in local optimal solution, rather than global
optimal solution. In this chapter, we first develop a clear and formal
definition of the CoD problem, next we focus on feature extraction techniques
and categories, then we provide a general overview of meta-heuristic
algorithms, its terminology, and desirable properties of evolutionary
algorithms.
