A Survey on Techniques of Improving Generalization Ability of Genetic Programming Solutions

TL;DR

This survey reviews various techniques used in genetic programming to enhance generalization, focusing on bloat and over-fitting control methods, and discusses their limitations to aid practitioners in selecting suitable approaches.

Contribution

It provides a comprehensive classification and analysis of existing bloat and over-fitting control techniques in genetic programming, highlighting their limitations.

Findings

Classified existing techniques for bloat and over-fitting control.

Discussed limitations of current approaches.

Provided guidance for selecting appropriate generalization enhancement methods.

Abstract

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.