Computational study of a family of mixed-integer quadratic programming problems
Mathematical Programming: Series A and B
Heuristics for cardinality constrained portfolio optimisation
Computers and Operations Research
Tabu Search
Journal of Global Optimization
Local Search Techniques for Constrained Portfolio SelectionProblems
Computational Economics
Extending Population-Based Incremental Learning to Continuous Search Spaces
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
DE/EDA: a new evolutionary algorithm for global optimization
Information Sciences—Informatics and Computer Science: An International Journal
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Portfolio Management with Heuristic Optimization (Advances in Computational Management Science)
Portfolio Management with Heuristic Optimization (Advances in Computational Management Science)
Improved Particle Swarm Optimization for Realistic Portfolio Selection
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Lagrangian relaxation procedure for cardinality-constrained portfolio optimization
Optimization Methods & Software
Expert Systems with Applications: An International Journal
Algorithm for cardinality-constrained quadratic optimization
Computational Optimization and Applications
Effects of learning rate on the performance of the population based incremental learning algorithm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An Estimation of Distribution Algorithm Based Portfolio Selection Approach
TAAI '10 Proceedings of the 2010 International Conference on Technologies and Applications of Artificial Intelligence
Constrained Portfolio Selection using Particle Swarm Optimization
Expert Systems with Applications: An International Journal
An evolutionary algorithm with guided mutation for the maximum clique problem
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Steepest ascent hill climbing for portfolio selection
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Population based Local Search for university course timetabling problems
Applied Intelligence
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Since Markowitz's seminal work on the mean-variance model in modern portfolio theory, many studies have been conducted on computational techniques and recently meta-heuristics for portfolio selection problems. In this work, we propose and investigate a new hybrid algorithm integrating the population based incremental learning and differential evolution algorithms for the portfolio selection problem. We consider the extended mean-variance model with practical trading constraints including the cardinality, floor and ceiling constraints. The proposed hybrid algorithm adopts a partially guided mutation and an elitist strategy to promote the quality of solution. The performance of the proposed hybrid algorithm has been evaluated on the extended benchmark datasets in the OR Library. The computational results demonstrate that the proposed hybrid algorithm is not only effective but also efficient in solving the mean-variance model with real world constraints.