doi:10.1145/1068009.1068307">
 

Title

Preventing overfitting in GP with canary functions

Document Type

Conference Proceeding

Publication Date

2005

Department/School

Computer Science

Abstract

Overfitting is a fundamental problem of most machine learning techniques, including genetic programming (GP). Canary functions have been introduced in the literature as a concept for preventing overfitting by automatically recognizing when it starts to occur. This paper presents a simple scheme for implementing canary functions using cross-validation. The effectiveness of this technique is demonstrated by applying it to the numeric regression problem. A list of conditions and criteria for applying this technique to other problem domains is also identified. Other strategies for dealing with overfitting in GP are discussed.

Link to Published Version

doi:10.1145/1068009.1068307