Customized Evolutionary Expensive Optimization: Efficient Search and Surrogate Strategies for Continuous and Categorical Variables

------------------------------- Reference --------------------------------

Z.W, L. X.,et al. Customized Evolutionary Expensive Optimization: Efficient Search and Surrogate Strategies for Continuous and Categorical Variables in IEEE Transactions on Systems Man Cybernetics-Systems.

------------------------------- Copyright --------------------------------

Permission is granted to copy and use this code for research, noncommercial purposes, provided this copyright notice is retained and the origin of the code is cited. The code is provided "as is" and without any warranties, express or implied.

If you have further questions, please contact me. My email address is: lindong.xie@connect.polyu.hk

If you would like to run CEEO on benchmark experiments, please execute Run_CEEO_for_Benchmarks_Experiment.py. To apply CEEO for CNN parameter optimization, please execute Run_CEEO_for_CNN_Experiment.py.

• python == 3.8
• smt == 1.1.0
• numpy == 1.24.4
• scipy == 1.8.1

• pandas == 1.5.0
• torch == 2.1.0
• torchvision == 0.16.0