Modeling multiple-response environmental and manufacturing characteristics of EDM process
Among the machining operations, Electrical discharge machining (EDM) process is widely
used in production industries because of its ability to machine the materials of any hardness …
used in production industries because of its ability to machine the materials of any hardness …
Structural risk minimization-driven genetic programming for enhancing generalization in symbolic regression
Generalization ability, which reflects the prediction ability of a learned model, is an important
property in genetic programming (GP) for symbolic regression. Structural risk minimization …
property in genetic programming (GP) for symbolic regression. Structural risk minimization …
A hybrid-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process
Recent years have seen various rapid prototy** (RP) processes such as fused deposition
modelling (FDM) and three-dimensional printing being used for fabricating prototypes …
modelling (FDM) and three-dimensional printing being used for fabricating prototypes …
A survey of statistical machine learning elements in genetic programming
Modern genetic programming (GP) operates within the statistical machine learning (SML)
framework. In this framework, evolution needs to balance between approximation of an …
framework. In this framework, evolution needs to balance between approximation of an …
GECCO'2022 Symbolic Regression Competition: Post-Analysis of the Operon Framework
B Burlacu - Proceedings of the Companion Conference on Genetic …, 2023 - dl.acm.org
Operon is a C++ framework for symbolic regression with the ability to perform local search
by optimizing model coefficients using the Levenberg-Marquardt algorithm. This …
by optimizing model coefficients using the Levenberg-Marquardt algorithm. This …
Improving generalisation of genetic programming for symbolic regression with structural risk minimisation
Generalisation is one of the most important performance measures for any learning
algorithm, no exception to Genetic Programming (GP). A number of works have been …
algorithm, no exception to Genetic Programming (GP). A number of works have been …
Mathematical modelling of burr height of the drilling process using a statistical-based multi-gene genetic programming approach
Drilling is one of the important machining processes performed extensively in production
industry. Literature emphasises that the output process parameters such as burr height …
industry. Literature emphasises that the output process parameters such as burr height …
Tikhonov regularization as a complexity measure in multiobjective genetic programming
J Ni, P Rockett - IEEE Transactions on Evolutionary …, 2014 - ieeexplore.ieee.org
In this paper, we propose the use of Tikhonov regularization in conjunction with node count
as a general complexity measure in multiobjective genetic programming. We demonstrate …
as a general complexity measure in multiobjective genetic programming. We demonstrate …
Classification-driven model selection approach of genetic programming in modelling of turning process
Turning is a widely used machining process, but the process complexity and uncertainty
lead to empirical modelling techniques being preferred over physics-based models for …
lead to empirical modelling techniques being preferred over physics-based models for …
Evolving functional expression of permeability of fly ash by a new evolutionary approach
The influence of stress, which is one of the constitutive variables that governs unsaturated
soil behavior, on the permeability has been recognized by various researchers. Stress factor …
soil behavior, on the permeability has been recognized by various researchers. Stress factor …