2004
DOI: 10.1007/978-3-642-17022-5_33
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Evolutionary Algorithms and the Cardinality Constrained Portfolio Optimization Problem

Abstract: Abstract. While the unconstrained portfolio optimization problem can be solved efficiently by standard algorithms, this is not the case for the portfolio optimization problem with additional real world constraints like cardinality constraints, buy-in thresholds, roundlots etc. In this paper we investigate two extensions to Evolutionary Algorithms (EA) applied to the portfolio optimization problem. First, we introduce a problem specific EA representation and then we add a local search for feasible solutions to … Show more

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Cited by 76 publications
(53 citation statements)
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“…These parameters for the operators were selected to allow a fair comparison. The general parameters were found in preliminary experiments [14].…”
Section: Multi-objective Evolutionary Algorithmmentioning
confidence: 99%
“…These parameters for the operators were selected to allow a fair comparison. The general parameters were found in preliminary experiments [14].…”
Section: Multi-objective Evolutionary Algorithmmentioning
confidence: 99%
“…Alternatively, some funds may look very attractive once their low risk was taken into account. Streichert et al (2004) investigated the same portfolio optimization problem using evolutionary algorithms by considering the cardinality constrained. Maringer and Kellerer (2003) studied the same optimization of portfolios using a hybrid local search algorithm.…”
Section: Introductionmentioning
confidence: 99%
“…They also showed the differences arising in the shape of this efficient frontier when such constraints imposed and solved the resulted model using three heuristic algorithms based upon genetic algorithms, tabu search and simulated annealing for locating the cardinality constrained efficient frontier. Streichert et al (2004) also solved the same portfolio optimization problem using evolutionary algorithms by considering the cardinality constrained. Maringer and Kellerer (2003) considered the same optimization of cardinality constrained portfolios with a hybrid local search algorithm.…”
Section: Introductionmentioning
confidence: 99%