  In the field of empirical modeling using Genetic Programming (GP), it is
important to evolve solution with good generalization ability. Generalization
ability of GP solutions get affected by two important issues: bloat and
over-fitting. We surveyed and classified existing literature related to
different techniques used by GP research community to deal with these issues.
We also point out limitation of these techniques, if any. Moreover, the
classification of different bloat control approaches and measures for bloat and
over-fitting are also discussed. We believe that this work will be useful to GP
practitioners in following ways: (i) to better understand concepts of
generalization in GP (ii) comparing existing bloat and over-fitting control
techniques and (iii) selecting appropriate approach to improve generalization
ability of GP evolved solutions.
