Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey

TL;DR

This survey reviews quantum-inspired evolutionary algorithms (QIEAs) for feature subset selection, highlighting their quantum concepts, heuristic variations, and common quantum gates, while identifying open research challenges.

Contribution

It provides a comprehensive analysis of 56 QIEA-based methods for feature selection, detailing their heuristics, objective functions, and quantum gate usage, and suggests future research directions.

Findings

QIEAs improve diversity and global search capabilities.

Various quantum gates, especially rotation gates, are commonly used.

Open problems in QIEA research are identified.

Abstract

The clever hybridization of quantum computing concepts and evolutionary algorithms (EAs) resulted in a new field called quantum-inspired evolutionary algorithms (QIEAs). Unlike traditional EAs, QIEAs employ quantum bits to adopt a probabilistic representation of the state of a feature in a given solution. This unprecedented feature enables them to achieve better diversity and perform global search, effectively yielding a tradeoff between exploration and exploitation. We conducted a comprehensive survey across various publishers and gathered 56 papers. We thoroughly analyzed these publications, focusing on the novelty elements and types of heuristics employed by the extant quantum-inspired evolutionary algorithms (QIEAs) proposed to solve the feature subset selection (FSS) problem. Importantly, we provided a detailed analysis of the different types of objective functions and popular quantum gates, i.e., rotation gates, employed throughout the literature. Additionally, we suggested several open research problems to attract the attention of the researchers.