Paper Info
Title
A New Population Initialization Approach Based on Bordered Hessian for Portfolio Optimization Problems
Abstract
In the portfolio optimization problems, the proportion-weighted combination in a portfolio is represented as a real-valued array between 0 and 1. While applying any evolutionary algorithm, however, the algorithm hardly takes the ends of a given real value. It means that the evolutionary algorithms have a problem that they cannot give the not-selected asset whose weight is represented as 0. In order to avoid this problem, we propose a new population initialization approach using the extreme point of the bordered Hessian and then apply our approach to the initial population of GA for the portfolio optimization problems in this paper. In the numerical experiments, we show that our method employing the population initialization approach and GA works very well for the portfolio optimizations even if the portfolio consists of the large number of assets.
Year
DOI
Venue
2013
10.1109/SMC.2013.232
Systems, Man, and Cybernetics
Keywords
Field
DocType
not-selected asset,bordered hessian,numerical experiment,new population initialization approach,portfolio optimization problems,large number,population initialization approach,portfolio optimization problem,evolutionary algorithm,proportion-weighted combination,extreme point,initial population,investment,genetic algorithms
Extreme point,Population,Mathematical optimization,Evolutionary algorithm,Computer science,Hessian matrix,Portfolio,Portfolio optimization,Artificial intelligence,Initialization,Machine learning,Genetic algorithm
Conference
ISSN
Citations 
PageRank 
1062-922X
0
0.34
References 
Authors
7
4
Name
Order
Citations
PageRank
Yukiko Orito1137.69
Yoshiko Hanada22011.42
Shunsuke Shibata300.68
Hisashi Yamamoto492.93