@inproceedings{d8e3d0f190e1459abd5d5ed84a6147aa,
title = "An evolutionary algorithm with a new coding scheme for multi-objective portfolio optimization",
abstract = "A portfolio optimization problem involves optimal allocation of finite capital to a series of assets to achieve an acceptable trade-off between profit and risk in a given investment period. In the paper, the extended Markowitz{\textquoteright}s mean-variance portfolio optimization model is studied. A major challenge with this model is that it contains both discrete and continuous decision variables, which represent the assignment and allocation of assets respectively. To deal with this hard problem, this paper proposes an evolutionary algorithm with a new coding scheme that converts discrete variables into continuous ones. By this way, the mixed variables can be handled, and some of the constraints are naturally satisfied. The new approach is empirically studied and the experiment results indicate its efficiency.",
keywords = "Constraints handling, Mixed variables, Multi-objective portfolio",
author = "Yi Chen and Aimin Zhou and Rongfang Zhou and Peng He and Yong Zhao and Lihua Dong",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 11th International Conference on Simulated Evolution and Learning, SEAL 2017 ; Conference date: 10-11-2017 Through 13-11-2017",
year = "2017",
doi = "10.1007/978-3-319-68759-9\_9",
language = "英语",
isbn = "9783319687582",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "97--109",
editor = "Xiaodong Li and Mengjie Zhang and Qingfu Zhang and Martin Middendorf and Tan, \{Kay Chen\} and Ying Tan and Yaochu Jin and Yuhui Shi and Ke Tang",
booktitle = "Simulated Evolution and Learning - 11th International Conference, SEAL 2017, Proceedings",
address = "德国",
}