: This package includes the python code of the EAMC algorithm  for maximizing monotone set functions with monotone cost constraints. EAMC employs an evolutionary algorithm to solve a surrogate objective integrating the original objective function and the cost function. EAMC achieves the best-known polynomial-time approximation guarantee, which overcomes the limitation, i.e., no polynomial-time approximation guarantee, of our previous algorithm POMC . A Readme file and example files are included in the package.
:  Chao Bian, Chao Feng, Chao Qian, and Yang Yu. An Efficient Evolutionary Algorithm for Subset Selection with General Cost Constraints. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.
 Chao Qian, Jing-Cheng Shi, Yang Yu, and Ke Tang. On Subset Selection with General Cost Constraints. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.
: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Dr. Chao Qian (email@example.com).
: The package was developed with python.
: This package was developed by Mr. Chao Bian (firstname.lastname@example.org) and Mr. Chao Feng (email@example.com). For any problem concerning the code, please feel free to contact them.