Combining-multi-objective-genetic-algorithm-and-neural-network-dynamically

Combining multi-objective genetic algorithm and neural network dynamically for the complex optimization problems in physics

These files save the raw data created by DNMOGA, NBMOGA and NSGA-II, and their titles are related to the figures in paper.

Name		Name	Last commit message	Last commit date
Latest commit History 55 Commits
__pycache__		__pycache__
config		config
data		data
figures		figures
project/Enlarged_mode		project/Enlarged_mode
C++thread052301.exe		C++thread052301.exe
MLBGA.py		MLBGA.py
README.md		README.md
WPL_NN.py		WPL_NN.py
WPL_get_init_temp.py		WPL_get_init_temp.py
WPL_others_inSS.py		WPL_others_inSS.py
WPL_simulation_SS.py		WPL_simulation_SS.py
WPL_test_os.py		WPL_test_os.py
WPL_transformer.py		WPL_transformer.py
adam.py		adam.py
base.py		base.py
cstmanager.py		cstmanager.py
cstworker.py		cstworker.py
find_variated.py		find_variated.py
gd.py		gd.py
globalconfmanager.py		globalconfmanager.py
logger.py		logger.py
myAlgorithm.py		myAlgorithm.py
projectconfmanager.py		projectconfmanager.py
projectutil.py		projectutil.py
result.py		result.py
runme_POP.bat		runme_POP.bat
runme_enlarged_mode.bat		runme_enlarged_mode.bat
runme_hom_mode.bat		runme_hom_mode.bat
temp.txt		temp.txt
temp1.txt		temp1.txt
trustregion_new.py		trustregion_new.py
worker.py		worker.py
yfunction.py		yfunction.py

peilin-wang-git/Combining-multi-objective-genetic-algorithm-and-neural-network-dynamically