Evolving genetic programming models of higher generalization ability in modelling of turning process

Abstract

Purpose

Generalization ability of genetic programming (GP) models relies highly on the choice of parameter settings chosen and the fitness function used. The purpose of this paper is to conduct critical survey followed by quantitative analysis to determine the appropriate parameter settings and fitness function responsible for evolving the GP models with higher generalization ability.

Design/methodology/approach

For having a better understanding about the parameter settings, the present work examines the notion, applications, abilities and the issues of GP in the modelling of machining processes. A gamut of model selection criteria have been used in fitness functions of GP, but, the choice of an appropriate one is unclear. In this work, GP is applied to model the turning process to study the effect of fitness functions on its performance.

Findings

The results show that the fitness function, structural risk minimization (SRM) gives better generalization ability of the models than those of other fitness functions.

Originality/value

This study is of its first kind where two main contributions are listed addressing the need of evolving GP models with higher generalization ability. First is the survey study conducted to determine the parameter settings and second, the quantitative analysis for unearthing the best fitness function.

Citation

Garg, A. and Tai, K. (2015), "Evolving genetic programming models of higher generalization ability in modelling of turning process", Engineering Computations, Vol. 32 No. 8, pp. 2216-2234. https://doi.org/10.1108/EC-12-2014-0252