ITM Web Conf.
Volume 11, 20172017 International Conference on Information Science and Technology (IST 2017)
|Number of page(s)||9|
|Section||Session I: Computational Intelligence|
|Published online||23 May 2017|
Application of the Improved Differential Evolution Algorithm in Portfolio
1 Lushan College of Guangxi University Science and Technology, Liuzhou, Guangxi 545616, China ;
2 College of Mathematics and Computer Science, Guangxi University for Nationalities, Nanning 530006, China ;
3 College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, Guangxi 530006, China
a Corresponding author: email@example.com
Aiming at the NP hard problem of portfolio optimization, an improved differential evolution algorithm is proposed. In this algorithm, the mutation operator and crossover operator are set up adaptively, and then according to the characteristics of the mutation itself, two kinds of mutation operators with global search ability and local search ability are improved .The improved algorithm can improve the convergence speed and ensure the precision of the algorithm. Through five stocks of the same type and 20 different types of stocks for empirical analysis, the results show that the proposed algorithm has a certain guiding role in solving the problem of portfolio optimization.
© Owned by the authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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