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The paper reviews classification algorithms based on Gene Expression Programming (GEP) used for mining the real-life datasets. Our aim is to show, chronologically, most important developments as well as the current state-of-the-art in the area of GEP-based classifiers, with a view to attract further real life applications. We begin with reviewing approaches to building basic, stand alone, GEP classifiers and eventually, combining them into the classifier ensemble. In the following part of the paper we describe and illustrate with example several hybrid solutions where GEP is integrated with other methods. Next, we review specialized and dedicated methods including multiple criteria and incremental GEP-based classification tools. Final part of the paper reviews specialized GEP-based classifiers used to mine the real-life datasets. [ABSTRACT FROM AUTHOR]