Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A model for portfolio selection with order of expected returns
Computers and Operations Research
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Generating trading rules on the stock markets with genetic programming
Computers and Operations Research
Decision Support Systems - Special issue: Data mining for financial decision making
A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications: An International Journal
Neural network-based mean-variance-skewness model for portfolio selection
Computers and Operations Research
A genetic programming model for bankruptcy prediction: Empirical evidence from Iran
Expert Systems with Applications: An International Journal
A portfolio optimization model using Genetic Network Programming with control nodes
Expert Systems with Applications: An International Journal
Portfolio algorithm based on portfolio beta using genetic algorithm
Expert Systems with Applications: An International Journal
Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry
Expert Systems with Applications: An International Journal
A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review
Expert Systems with Applications: An International Journal
Credit portfolio management using two-level particle swarm optimization
Information Sciences: an International Journal
Hi-index | 12.05 |
The survey of the relevant literatures shows that there have been many studies for portfolio optimization problems and that the number of studies which have investigated the optimum portfolio using evolutionary computation is quite large. But, almost none of these studies deals with genetic relation algorithm (GRA), where GRA is one of the evolutionary methods with graph structure. This study presents an approach to large-scale portfolio optimization problems using GRA with a new operator, called guided mutation. In order to pick up the most efficient portfolio, GRA considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in each individual of GRA. Guided mutation generates offspring according to the average value of correlation coefficients in each individual, which means to enhance the exploitation ability of evolution of GRA. A genetic relation algorithm with guided mutation (GRA/G) for the portfolio optimization is proposed in this paper. Genetic network programming (GNP), which was proposed in our previous research, is used to validate the performance of the portfolio generated with GRA/G. The results show that GRA/G approach is successful in portfolio optimization.