抄録
Though there are numerous approaches developed currently, exploring the practical applications of estimation of distribution algorithm (EDA) has been reported to be one of the most important challenges in this field. This paper is dedicated to extend EDA to solve one of the most active research problems - stock trading, which has been rarely revealed in the EDA literature. A recent proposed graph-based EDA called reinforced probabilistic model building genetic network programming (RPMBGNP) is investigated to create stock trading rules. With its distinguished directed graph-based individual structure and the reinforcement learning-based probabilistic modeling, we demonstrate the effectiveness of RPMBGNP for the stock trading task through real-market stock data, where much higher profits are obtained than traditional non-EDA models.
| 本文言語 | English |
|---|---|
| ホスト出版物のタイトル | Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 |
| 出版社 | Institute of Electrical and Electronics Engineers Inc. |
| ページ | 731-738 |
| ページ数 | 8 |
| ISBN（印刷版） | 9781479914883 |
| DOI | |
| 出版ステータス | Published - 2014 9 16 |
| イベント | 2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing 継続期間: 2014 7 6 → 2014 7 11 |
Other
| Other | 2014 IEEE Congress on Evolutionary Computation, CEC 2014 |
|---|---|
| City | Beijing |
| Period | 14/7/6 → 14/7/11 |
ASJC Scopus subject areas
- 人工知能
- 計算理論と計算数学
- 理論的コンピュータサイエンス