  Biclustering is an unsupervised machine learning technique that
simultaneously clusters rows and columns in a data matrix. Biclustering has
emerged as an important approach and plays an essential role in various
applications such as bioinformatics, text mining, and pattern recognition.
However, finding significant biclusters is an NP-hard problem that can be
formulated as an optimization problem. Therefore, different metaheuristics have
been applied to biclustering problems because of their exploratory capability
of solving complex optimization problems in reasonable computation time.
Although various surveys on biclustering have been proposed, there is a lack of
a comprehensive survey on the biclustering problem using metaheuristics. This
chapter will present a survey of metaheuristics approaches to address the
biclustering problem. The review focuses on the underlying optimization methods
and their main search components: representation, objective function, and
variation operators. A specific discussion on single versus multi-objective
approaches is presented. Finally, some emerging research directions are
presented.
