EAMC

Description : This package includes the python code of the EAMC algorithm [1] 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 [2]. A Readme file and example files are included in the package.

References: [1] 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. [2] 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.

ATTN: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Dr. Chao Qian (qianc@lamda.nju.edu.cn).

Requirement: The package was developed with python.

ATTN2: This package was developed by Mr. Chao Bian (chaobian12@gmail.com) and Mr. Chao Feng (chaofeng@mail.ustc.edu.cn). For any problem concerning the code, please feel free to contact them.

Download: code (1MB)