- author = "Akhil Garg and Kang Tai",
- title = "Evolving genetic programming models of higher generalization ability in modelling of turning process",
- journal = "Engineering Computations",
- year = "2015",
- volume = "32",
- number = "8",
- pages = "2216--2234",
- month = nov,
- keywords = "genetic algorithms, genetic programming, surface roughness",
- ISSN = "0264-4401",
-
URL = "
https://www.emerald.com/insight/content/doi/10.1108/EC-12-2014-0252/full/html",
-
DOI = "
doi:10.1108/EC-12-2014-0252",
-
abstract = "Generalisation 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 modeling 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. 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.",
- notes = "Mechanical and Aerospace engineering, Nanyang Technological University, Singapore, Singapore",
