Generalizing the notion of schema in genetic algorithms
Artificial Intelligence
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Computation of mean-semivariance efficient sets by the Critical Line Algorithm
Annals of Operations Research
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
A heuristic algorithm for a portfolio optimization model applied to the Milan stock market
Computers and Operations Research
An introduction to genetic algorithms
An introduction to genetic algorithms
A model for portfolio selection with order of expected returns
Computers and Operations Research
Heuristics for cardinality constrained portfolio optimisation
Computers and Operations Research
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Local Search Techniques for Constrained Portfolio SelectionProblems
Computational Economics
Index fund selections with genetic algorithms and heuristic classifications
Computers and Industrial Engineering
Using genetic algorithm to support portfolio optimization for index fund management
Expert Systems with Applications: An International Journal
Portfolio algorithm based on portfolio beta using genetic algorithm
Expert Systems with Applications: An International Journal
Portfolio selection based on technical trading rules optimized with a genetic algorithm
INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
An optimization model of the portfolio adjusting problem with fuzzy return and a SMO algorithm
Expert Systems with Applications: An International Journal
Meta heuristics for dependent portfolio selection problem considering risk
Expert Systems with Applications: An International Journal
Constrained Portfolio Selection using Particle Swarm Optimization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Mean-variance models for portfolio selection subject to experts' estimations
Expert Systems with Applications: An International Journal
Time-stamped resampling for robust evolutionary portfolio optimization
Expert Systems with Applications: An International Journal
Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review
Expert Systems with Applications: An International Journal
An approach to portfolio selection using an ARX predictor for securities' risk and return
Expert Systems with Applications: An International Journal
A risk index model for multi-period uncertain portfolio selection
Information Sciences: an International Journal
A hybrid algorithm for constrained portfolio selection problems
Applied Intelligence
Bacterial Foraging Optimization Approach to Portfolio Optimization
Computational Economics
Hi-index | 12.06 |
Heuristic algorithms strengthen researchers to solve more complex and combinatorial problems in a reasonable time. Markowitz's Mean-Variance portfolio selection model is one of those aforesaid problems. Actually, Markowitz's model is a nonlinear (quadratic) programming problem which has been solved by a variety of heuristic and non-heuristic techniques. In this paper a portfolio selection model which is based on Markowitz's portfolio selection problem including three of the most important limitations is considered. The results can lead Markowitz's model to a more practical one. Minimum transaction lots, cardinality constraints (both of which have been presented before in other researches) and market (sector) capitalization (which is proposed in this research for the first time as a constraint for Markowitz model), are considered in extended model. No study has ever proposed and solved this expanded model. To solve this mixed-integer nonlinear programming (NP-Hard), a corresponding genetic algorithm (GA) is utilized. Computational study is performed in two main parts; first, verifying and validating proposed GA and second, studying the applicability of presented model using large scale problems.