Paper Info
Title
Use of heuristic rules in evolutionary methods for the selection of optimal investment portfolios
Abstract
A novel hybrid algorithm that combines evolutionary algorithms, quadratic programming, and a specially devised pruning heuristic is proposed for the selection of cardinality- constrained optimal portfolios. The framework used is the standard Markowitz mean-variance formulation for portfolio selection with constraints of practical interest, such as minimum and maximum investments per asset and/or on groups of assets. The use of cardinality constraints transforms portfolio selection into an NP-hard mixed-integer quadratic optimization problem that is difficult to solve by standard methods. An implementation of the algorithm that employs a genetic algorithm with a set representation, an appropriately defined mutation operator and Random Assortment Recombination for crossover (RAR-GA) is compared with implementations using various estimation of distribution algorithms (EDAs). Without the pruning heuristic, RAR-GA is superior to the implementations with EDAs in terms of both accuracy and efficiency. The incorporation of the pruning heuristic leads to a significant decrease in computation times and makes EDAs competitive with RAR-GA.
Year
DOI
Venue
2007
10.1109/CEC.2007.4424474
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
evolutionary methods,evolutionary computation,quadratic programming,portfolio selection,optimal investment portfolios,pruning heuristic,cardinality constraints transforms,np-hard mixed-integer quadratic optimization,computational complexity,random assortment recombination,genetic algorithm,investment,cardinality-constrained optimal portfolio,heuristic rules,mutation operator,distribution algorithms,standard markowitz mean-variance formulation,constrained optimization,hybrid algorithm,quadratic program,evolutionary algorithm,quadratic optimization,estimation of distribution algorithm
EDAS,Mathematical optimization,Heuristic,Killer heuristic,Hybrid algorithm,Evolutionary algorithm,Computer science,Evolutionary computation,Artificial intelligence,Null-move heuristic,Genetic algorithm,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-1340-9
5
0.48
References 
Authors
7
2
Name
Order
Citations
PageRank
Rubén Ruiz-Torrubiano1964.33
Alberto Suárez248722.33