Heuristics for cardinality constrained portfolio optimisation
Computers and Operations Research
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
A tree-based GA representation for the portfolio optimization problem
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multiobjective robustness for portfolio optimization in volatile environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Global optimization of higher order moments in portfolio selection
Journal of Global Optimization
Using memetic algorithms to improve portfolio performance in static and dynamic trading scenarios
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An Improved Particle Swarm Optimization for the Constrained Portfolio Selection Problem
CINC '09 Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01
Heuristic Optimization for Portfolio Management [Application Notes]
IEEE Computational Intelligence Magazine
An EA for portfolio selection over multiple investment periods with exponential transaction costs
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A hybrid algorithm for constrained portfolio selection problems
Applied Intelligence
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The construction of a portfolio in the financial field is a problem faced by individuals and institutions worldwide. In this paper we present an approach to solve the portfolio selection problem with the Steepest Ascent Hill Climbing algorithm. There are many works reported in the literature that attempt to solve this problem using evolutionary methods. We analyze the quality of the solutions found by a simpler algorithm and show that its performance is similar to a Genetic Algorithm, a more complex method. Real world restrictions such as portfolio value and rounded lots are considered to give a realistic approach to the problem.