Evolutionary Computation, Optimization and Learning Algorithms for Data Science

TL;DR

This paper discusses the application of evolutionary algorithms to address the curse of dimensionality in data science, emphasizing optimization, feature extraction, and the robustness of nature-inspired methods for large-scale data analysis.

Contribution

It provides a formal definition of the curse of dimensionality, reviews feature extraction techniques, and offers an overview of meta-heuristic algorithms and their properties in data science.

Findings

Evolutionary algorithms are effective for high-dimensional data optimization.

Feature extraction techniques help mitigate the curse of dimensionality.

Meta-heuristic algorithms are robust and suitable for complex data analysis tasks.

Abstract

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.