Compositional Kernel Learning using Tree-based Genetic Programming (CPKL_Tree-GEP) for Gaussian Proecess Regression
The source codes in this repository are uploaded for the replication of results section for the following work.
- Jin, S. Compositional kernel learning using tree-based genetic programming for Gaussian process regression. Struct Multidisc Optim 62, 1313–1351 (2020). https://doi.org/10.1007/s00158-020-02559-7
The uploaded source codes are only working for the 2-dimensional Mathematical test function ("Branin function"). The source codes cannot be opened to public yet. To apply the method to your own application, please contact to "seungsab@gmail.com".
Three methods can be implemented by running the following m-files
- Run_Singel_kernel_GPML.m: Gaussian Process using single kernels
- Run_AUTO_GPR_GPML_HCPKL.m: Gaussian Process using Hierarchical CPK
- Run_AUTO_GPR_GPML_GPlab.m: Gaussian Process using CPKL_Tree-GEP
All results given in the manuscript are in the folders as "1_Test_functions" and "2_Physical_models"
- To see the results from Matehmatical test functions, run "Plot_Results_Test_function.m"
- To see the results from Physcial models, run "Plot_Results_Physcial_models.m"
If you have any questions, please feel free to contact me: seungsab@gmail.com (Seung-Seop Jin).
Note) These source codes are developed in MATLAB R2018b. They may not be working below MATLAB R2018b.
In order to run the source codes, you need the following MATLAB toolboxes.
[1] GPML toolbox (for Gaussian process): http://www.gaussianprocess.org/gpml/code/matlab/doc/
- C.E. Rasmussen, C.K.I. Williams, Gaussian processes for machine learning, MIT Press, Cambridge, Mass., 2006.
[2] GPlab toolbox (for Tree-based Genetic programing): http://gplab.sourceforge.net/
- William E, Northern J Genetic Programming Lab (GPLab) Tool Set Version 3.0. In: 2008 IEEE Region 5 Conference, 17-20 April 2008 2008. pp 1-6. doi:10.1109/TPSD.2008.4562729