EPFA-CLS : Deriving Controllable Local Optimal Solutions through an Environment Parameter Fixed Algorithm

This is the official repository for our recent work: pdf

Overview

EPFA-CLS: An algorithm for finding the local optimal control parameters (CLS) in a deep learning regression network for a specific set of environment parameters.
Control Parameter: The parameter to be controlled (optimized).
Environment Parameter: The parameter that cannot be controlled (constant)

This algorithm can be used in the following scenarios:

Boston housing dataset

The figure represents the distribution of optimized results obtained by setting all values from the Boston Housing dataset as initial values and introducing four random control parameters. The histograms depict the control parameters before and after optimization using each of the NOX, RM, DIS, and TAX control parameters, compared to the initial values shown in black. The distribution of Boston Housing Prices is shown in black to observe the changes in housing prices after optimization. The red color represents NOX, yellow represents RM, blue represents DIS, and green represents TAX.

Optimal course dataset

The 3D graph represents the relationship between the control parameters and the output of the function after fixing the environment parameters. It starts from the initial value represented by the red dot and eventually reaches the optimized point represented by the blue dot through the optimization process. In this context, the blue dot corresponds to the derived CLS.

Usage

0. Prepare the dataset

The dataset is currently available in Boston/data and OptimalCourse/data dirs. If you wish to create a custom dataset, you can do so by preparing the independent variables in labdata_x.csv and the dependent variables in labdata_y.csv and adding them to the same folder.

1. Training

The training process is a method to model the objective function, and it follows the same procedure as nonlinear regression based on deep learning.

The currently written code is designed for scenarios where the Boston dataset has 13 input variables, while the OptimalCourse dataset has 4 input variables. If you want to use the code as it is, please enter the following command in the terminal:

python ./Boston/train.py

or

python ./OptimalCourse/train.py

This command will execute the code and start the training process.

If you want to use a custom dataset, you need to modify the number of input variables in either Boston/Model.py or OptimalCourse/Model.py, depending on your chosen dataset. Additionally, if you wish to modify the batch size and number of epochs, you can do so by editing the corresponding sections in 'Boston/train.py'.

2. EPFA-CLS

To run the code for deriving the CLS using EPFA-CLS on the Boston and OptimalCourse datasets, you can use the following command:

For Boston dataset:

python ./Boston/EPFA-CLS.py

For OptimalCourse dataset:

python ./OptimalCourse/EPFA-CLS.py

In the Boston/EPFA-CLS.py and OptimalCourse/EPFA-CLS.py code files, you will find the part where the initial values are set. If you are using a custom dataset, you can modify the code to match the number of input variables accordingly.

Citation

If you think this implementation is useful for your work, please cite our paper:

@Article{jangEPFA_CLS,
          AUTHOR = {Jang, Ohtae and Jo, Sangho and Kim, Sungho},
          TITLE = {Deriving Controllable Local Optimal Solutions through an Environment Parameter Fixed Algorithm},
          JOURNAL = {Applied Sciences},
          VOLUME = {13},
          YEAR = {2023},
          NUMBER = {12},
          ARTICLE-NUMBER = {7110},
          URL = {https://www.mdpi.com/2076-3417/13/12/7110},
          ISSN = {2076-3417},
          DOI = {10.3390/app13127110}
}