Improvement Markowitz investment profolio model based on genetic algorithm

Author

Fei, Cai ; Da-Wei, Hu

Author_Institution

Comput. Dept., Inner Mongolia Educ. Entrance Examination Center, Huhhot, China

Volume

1

fYear

2010

fDate

21-24 May 2010

Abstract

An improved genetic algorithm based on analyzing the genetic algorithm performance bottlenecks is proposed. It applies Objective Adaptive Parallel Genetic Algorithm to solve the Markowitz model which is multi-objective limited investment restrictions. In this process, it discusses operator parameter design and studies dynamic adjustment group size and group diversity on the impact of the crossover and mutation probability technology. Matlab environment is used to compile programming for solving the model and simulating genetic algorithm search process. Research results show that improved genetic algorithm effectively improves the efficiency of the algorithm. This method is scientific and reasonable.

Keywords

genetic algorithms; investment; probability; search problems; Markowitz investment portfolio model; Matlab environment; crossover probability technology; dynamic adjustment group size; genetic algorithm performance bottlenecks; group diversity; multiobjective limited investment restrictions; mutation probability technology; objective adaptive parallel genetic algorithm; operator parameter design; search process; Algorithm design and analysis; Computer aided instruction; Computer science education; Design methodology; Genetic algorithms; Genetic mutations; Investments; Linear programming; Mathematical model; Partial response channels; MV model; crossover and mutation probability; genetic algorithm; group multiplicity;

fLanguage

English

Publisher

ieee

Conference_Titel

Future Computer and Communication (ICFCC), 2010 2nd International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-5821-9

Type

conf

DOI

10.1109/ICFCC.2010.5497721

Filename

5497721