Comparing Discrete and Continuous Genotypes on the Constrained Portfolio Selection Problem
Lecture notes in computer sciencePublished 1 January 2004
Felix Streichert, Holger Ulmer, Andreas Zell
Citations51
SJR quartileQ2
SJR score0.35
SNIP0.55
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A repair mechanism is applied and the impact of Lamarckism and the Baldwin Effect on several instances of the portfolio selection problem is examined and a Multi-Objective Evolutionary Algorithm is suggested to use.
Abstract
In financial engineering the problem of portfolio selection has drawn much attention in the last decades. But still unsolved problems remain, while on the one hand the type of model to use is still debated, even the most common models cannot be solved efficiently, if real world constraints are added. This is not only because the portfolio selection problem is multi-objective, but also because constraints may turn a formerly continuous problem into a discrete one. Therefore, we suggest to use a Multi-Objective Evolutionary Algorithm and compare discrete and continuous representations. To meet constraints we apply a repair mechanism and examine the impact of Lamarckism and the Baldwin Effect on several instances of the portfolio selection problem.
Keywords
Computer Science
IEEE Transactions on Evolutionary ComputationA fast and elitist multiobjective genetic algorithm: NSGA-II
47,759 Citations2002Kalyanmoy Deb, Amrit Pratap +2 more
This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
IEEE Transactions on Evolutionary ComputationMultiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
8,605 Citations1999Eckart Zitzler, Lothar Thiele
The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
Evolutionary ComputationMuiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
6,869 Citations1994N. Srinivas, Kalyanmoy Deb
Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously are investigated and suggested to be extended to higher dimensional and more difficult multiobjective problems.
Lecture notes in computer scienceA Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II
5,246 Citations2000Kalyanmoy Deb, Samir Agrawal +2 more
Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other elitist multi-objective EAs which pay special attention towards creating a diverse Paretimal front.
Journal of the American Statistical AssociationPortfolio Selection: Efficient Diversification of Investments.
3,745 Citations1962H. S. Houthakker, Harry M. Markowitz
international conference on Genetic algorithmsGenetic algorithms with sharing for multimodal function optimization
1,890 Citations1987David E. Goldberg, Jon T. Richardson
Journal of the Operational Research SocietyOR-Library: Distributing Test Problems by Electronic Mail
1,862 Citations1990J. E. Beasley
A system (OR-Library) that distributes test problems by electronic mail (e-mail) that has available test problems drawn from a number of different areas of operational research.
Financial Analysts JournalGlobal Portfolio Optimization
1,827 Citations1992Fischer Black, Robert B. Litterman
The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation
1,278 Citations2003Joshua Knowles, David Corne
It is argued that PAES may represent the simplest possible non-trivial algorithm capable of generating diverse solutions in the Pareto optimal set, and is intended as a good baseline approach against which more involved methods may be compared.
Computers & Operations ResearchHeuristics for cardinality constrained portfolio optimisation
928 Citations2000Tun-Jen Chang, Nigel Meade +2 more
This paper extends the standard mean-variance portfolio optimisation model to include cardinality constraints that limit a portfolio to have a specified number of assets, and to impose limits on the proportion of the portfolio held in a given asset.
Lecture notes in computer scienceParallel Problem Solving from Nature PPSN VI
557 Citations2000Marc Schoenauer, Kalyanmoy Deb +5 more
It is found that, with the same neighborhood, rectangular grids have some advantages in multimodal and epistatic problems, while square ones are more efficient for solving deceptive problems and for simple function optimization.
European Journal of Operational ResearchSimulated annealing for complex portfolio selection problems
345 Citations2003Yves Crama, Michaël Schyns
A simulated annealing approach to the solution of a complex portfolio selection model that arises when Markowitz’ classical mean–variance model is enriched with additional realistic constraints is described.
Lecture notes in computer scienceLamarckian evolution, the Baldwin effect and function optimization
322 Citations1994Darrell Whitley, V. Scott Gordon +1 more
It is shown that functions exist where simple genetic algorithms without learning as well as Lamarckian evolution converge to the same local optimum, while genetic search utilizing the Baldwin effect converges to the global optimum.
Artificial Neural Networks and Genetic Algorithms
82 Citations1991François E. Cellier
This chapter shall try to emulate learning mechanisms as they are believed to take place at the level of neurons of the human brain and evolutionary adaptation mechanisms as it is hypothesized to have shaped the authors' genetic code.
Artificial Neural Nets and Genetic AlgorithmsDistributed Genetic Algorithms with an Application to Portfolio Selection Problems
73 Citations1995A. Loraschi, Marco Tomassini +2 more
A PVM-based coarse-grained distributed genetic algorithm implemented on workstation clusters that easily outperforms sequential genetic algorithms and shows promise for difficult management applications.