  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.
