- Python 3.7.4 are used in the experiments.
- Check alias for python3 and edit
.shfiles. (If the alias ispython3, changepythontopython3. A better way is to usepyenvand setpyenv global 3.7.4.) - Following libraries are required,
- numpy
- scipy
- pandas
- matplotlib
- seaborn
- Open terminal
- Run
run.sh - Program will create a folder named
tmpand save simulation results in this folder - Copy folder
process_scriptintotmp - Enter
process_script - Run file
run.shinprocess_scriptfolder - Program will create
images,num_resandreportfolders, containing images, metrics and statistical issues, respectively.
The data is included in file DATA.tar.gz in this repository. After unzip, there are three folders namely, experimental_results, discussion and parameter_tuning_results, for the data (numerical values and graphs) of experiments, discussion and parameter tuning, respectively.
- This program does not contain statistical test. Please use R or other statistical software to implement Wilcoxon Rank-Sum Test.
- You may get a slight difference in numerical results, for machine epsilon issues. But the statistical conclusion should not change.
- The autoscripts can only run for all algorithms on all datasets. If you want to get results on single dataset or single algorithm, please edit and run
run_xxx.pyfiles. You may also edit processing scripts. However, to compute hypervolume, you may need get results for all algorithms on one dataset at first. - Please feel free to contact at [email protected] or [email protected] if you have any problem about the code.
.
├── dat ........................................ Folder for benchmark
│ ├── port1.txt .............................. Hangseng
│ ├── port2.txt .............................. DAX 100
│ ├── port3.txt .............................. FTSE 100
│ ├── port4.txt .............................. S&P 100
│ ├── port5.txt .............................. Nikkei
│ ├── portef1.txt ............................ Hangseng Pareto Front
│ ├── portef2.txt ............................ DAX 100 Pareto Front
│ ├── portef3.txt ............................ FTSE 100 Pareto Front
│ ├── portef4.txt ............................ S&P 100 Pareto Front
│ └── portef5.txt ............................ Nikkei Pareto Front
├── DATA.tar.gz ................................ Raw data for experimental results and discussion
├── dec_reprod.py .............................. Mutation for MOEA/D
├── domina.py .................................. Tools for NSGA-II
├── dom_reprod.py .............................. Mutation for NSGA-II
├── LICENSE
├── metric.py .................................. Evaluation metrics
├── moead.py ................................... NBI-MOEA/D framework
├── nbi_decomp.py .............................. NBI-style Tchebycheff
├── nsga2.py ................................... NSGA-II framework
├── plotting_sample.svg ........................ Sample plot for experimental results
├── prob.py .................................... Mean-Variance model
├── process_script ............................. Folder for post processing
│ ├── compute_metric.py ...................... Metrics calculation
│ ├── dat .................................... Folder for Pareto Front
│ │ ├── portef1.txt ........................ Hangseng Pareto Front
│ │ ├── portef2.txt ........................ DAX 100 Pareto Front
│ │ ├── portef3.txt ........................ FTSE 100 Pareto Front
│ │ ├── portef4.txt ........................ S&P 100 Pareto Front
│ │ └── portef5.txt ........................ Nikkei Pareto Front
│ ├── get_best.py ............................ Retrieve runs with best IGD
│ ├── metric.py .............................. Evaluation metrics
│ ├── plot.py ................................ Plotting scripts
│ ├── run.sh ................................. Run file to do post processing
│ └── stat.py ................................ Statistical processing
├── README.md .................................. Instruction
├── repair_tool.py ............................. Constraint handling technique
├── run_benchmark.sh ........................... Run file to test on one benchmark
├── run_dem.py ................................. Run MOEA/D-DEM
├── run_de.py .................................. Run MOEA/D-DE
├── run_ga.py .................................. Run MOEA/D-GA
├── run_lvxm.py ................................ Run MOEA/D-Levy
├── run_lvx.py ................................. Run LEVY
├── run_norm.py ................................ Run NORM
├── run_nsga2.py ............................... Run NSGA-II
├── run.sh ..................................... Run file to do whole experiments
└── run_unif.py ................................ Run UNIF
- Experiment I
- Experiment II
- Experiment I
- Experiment II
- Experiment I
- Experiment II
- Experiment I
- Experiment II
- Experiment I
- Experiment II
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