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A surrogate similarity measure for the mean-variance frontier optimisation problem under bound and cardinality constraints

Francisco Guijarro, Prodromos E. Tsinaslanidis
2019 Journal of the Operational Research Society  
This brings us to propose a surrogate similarity measure for the optimization of the constrained frontier, which differs from a previous proposal where no bound constraints were considered.  ...  Regarding the genetic algorithm, we propose an initial population to boost the convergence of the optimization process, whilst the adopted mutation and crossover genetic operators result in feasible individuals  ...  Acknowledgements We would like to thank two anonymous referees for their constructive comments and suggestions that substantially improved this article.  ... 
doi:10.1080/01605682.2019.1657367 fatcat:e7zrzzurcveyzfvjdub7rddch4

Evolutionary Algorithms and the Cardinality Constrained Portfolio Optimization Problem [chapter]

Felix Streichert, Holger Ulmer, Andreas Zell
2004 Operations Research Proceedings  
All algorithms are compared on the constrained and unconstrained portfolio optimization problem.  ...  like cardinality constraints, buy-in thresholds, roundlots etc.  ...  for searching for portfolios with limited cardinality.  ... 
doi:10.1007/978-3-642-17022-5_33 fatcat:kzkx7psc55e43awjg6fjb3egqy

A similarity measure for the cardinality constrained frontier in the mean–variance optimization model

Francisco Guijarro
2018 Journal of the Operational Research Society  
We assume that the closer the cardinality constrained frontier to the mean-variance frontier, the more appealing it is for the decision maker.  ...  We introduce a perceptual approach in the mean-variance cardinality constrained portfolio optimization problem by considering a novel similarity measure, which compares the cardinality constrained frontier  ...  Conclusions This paper deals with the problem of finding the optimal cardinality constrained frontier in the mean-variance space.  ... 
doi:10.1057/s41274-017-0276-6 fatcat:h2ywupidvrhcplxzlmcrtbswze

The Constrained Mean-Semivariance Portfolio Optimization Problem with the Support of a Novel Multiobjective Evolutionary Algorithm

K. Liagkouras, K. Metaxiotis
2013 Journal of Software Engineering and Applications  
The paper addresses the constrained mean-semivariance portfolio optimization problem with the support of a novel multi-objective evolutionary algorithm (n-MOEA).  ...  We also provide evidence for the robustness of the produced non-dominated solutions by carrying out, out-of-sample testing during both bull and bear market conditions on FTSE-100.  ...  Conclusions In this paper, we have proposed a novel MOEA for the solution of the constrained mean-semivariance portfolio optimization problem.  ... 
doi:10.4236/jsea.2013.67b005 fatcat:eaxetxancvaijh44lc2eery5ai

Portfolio optimization with an envelope-based multi-objective evolutionary algorithm

J. Branke, B. Scheckenbach, M. Stein, K. Deb, H. Schmeck
2009 European Journal of Operational Research  
In this paper, we propose to integrate an active set algorithm optimized for portfolio selection into a multi-objective evolutionary algorithm (MOEA).  ...  ., cardinality constraints which limit the number of different assets in a portfolio, or minimum buy-in thresholds.  ...  Acknowledgements We thank Ralph Steuer for providing us the large benchmark test cases.  ... 
doi:10.1016/j.ejor.2008.01.054 fatcat:g3m6ctz4ajftnpblt26ksmu274

A new Probe Guided Mutation operator and its application for solving the cardinality constrained portfolio optimization problem

K. Liagkouras, K. Metaxiotis
2014 Expert systems with applications  
cardinality constrained portfolio optimization problem (CCPOP).  ...  The results confirm that the PGM operator generates near optimal solutions that lie very close or in certain cases overlap with the TEF.  ...  In Section 2, a description of the highly disruptive Polynomial Mutation (PLM) is given and in Section 3 the proposed Probe Guided Mutation (PGM) and the formulation of the cardinality constrained portfolio  ... 
doi:10.1016/j.eswa.2014.03.051 fatcat:7xdcsresrzgxhekdnl4atjedqi

Evolutionary Computational Intelligence-Based Multi-Objective Sensor Management for Multi-Target Tracking

Shuang Liang, Yun Zhu, Hao Li, Junkun Yan
2022 Remote Sensing  
By modeling sensor selection as a multi-objective optimization problem, we develop a binary constrained evolutionary multi-objective algorithm based on non-dominating sorting and dynamically select a subset  ...  In multi-sensor systems (MSSs), sensor selection is a critical technique for obtaining high-quality sensing data.  ...  National Natural Science Foundation of China under grant numbers 62007022 and 61906146, the Natural Science Foundation of Shaanxi Province under grant number 2021JQ-209, and the Fundamental Research Funds for  ... 
doi:10.3390/rs14153624 fatcat:ey235nzfrzeulg5wc74alwbpre

A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization

Khin Lwin, Rong Qu, Graham Kendall
2014 Applied Soft Computing  
2014) A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization. Applied Soft Computing, 24 . pp. 757-772.  ...  In this paper, we studied the extended Markowitz's meanvariance portfolio optimization model. We considered the cardinality, quantity, pre-assignment and round lot constraints in the extended model.  ...  The authors would also like to thank anonymous reviewers for their helpful comments.  ... 
doi:10.1016/j.asoc.2014.08.026 fatcat:b4fvsnvk5zcprg42stwspo5wwi

Memetic search for identifying critical nodes in sparse graphs [article]

Yangming Zhou, Jin-Kao Hao, Fred Glover
2017 arXiv   pre-print
We also demonstrate the relevance of our algorithm for effectively solving a variant of the classic CNP, called the cardinality-constrained critical node problem.  ...  The double backbone-based crossover extends the idea of general backbone-based crossovers.  ...  Pullan for kindly sharing the source codes of the CNA1 and CNA2 algorithms.  ... 
arXiv:1705.04119v2 fatcat:dt3hzqg7aza3jjzoiz3h4u6xai

Using Viruses to Improve GAs [chapter]

Francesco Pappalardo
2005 Lecture Notes in Computer Science  
In this paper, we will introduce an evolutionary algorithm for finding approximate solutions to the Weighted Minimum Hitting Set Problem.  ...  "G" stands for greedy approach; "OPC" for one-point crossover; "TPC" for two-point crossover; "UFC" for uniform crossover.  ...  It is easy to see that for PS-rg's an optimal cover is given by the vertices of the second level. Thus the cardinality of the optimal cover is k + 2.  ... 
doi:10.1007/11539902_19 fatcat:ghib2hm7fbfh3dl72rkgek5pnq

The concept of genetic programming in organizing internal transport processes

Konrad Lewczuk
2015 Archives of Transport  
The possible structures of chromosome representing feasible solutions, methods of generating initial population, base genetic operators: selection and inheritance, crossover, mutation and fixing of individuals  ...  The organization of internal transport can be done through solving optimization task of scheduling internal transport process with allocation of human resources and equipment to the tasks.  ...  The range of integers is limited by the cardinality of sets U and C.  ... 
doi:10.5604/08669546.1169213 fatcat:tpixe7hgsraotikuyshkkabaka

Solving multi-scenario cardinality constrained optimization problems via multi-objective evolutionary algorithms

Xing Zhou, Huaimin Wang, Wei Peng, Bo Ding, Rui Wang
2019 Science China Information Sciences  
Cardinality constrained optimization problems (CCOPs) are fixed-size subset selection problems with applications in several fields.  ...  An MSCCOP is expected to optimize the objective value of each cardinality to support decision-making processes.  ...  The minimized single-scenario version 1) of a cardinality constrained optimization problem (CCOP), which searches for a cardinality-specified subset that optimally minimizes a function, can be expressed  ... 
doi:10.1007/s11432-018-9720-6 fatcat:bjllis3lxrch5g3ohkseaghktq

Density Conscious Subspace Clustering for High Dimensional Data using Genetic Algorithms

G. Sangeetha, mr. Sornamaheswari
2010 International Journal of Computer Applications  
It is capable of optimizing the number of clusters for tasks with well formed and separated clusters.  ...  Another approach for subspace clustering in high dimensional data is proposed using Genetic Approach.  ...  In this thesis, we propose to compute the upper bounds of unit counts for constraining the searching of dense units such that we add extra features into the DFP-tree for the computation.  ... 
doi:10.5120/1468-1983 fatcat:ynj7wqbjf5bzlmgacsg4bleo3y

A review on the current applications of genetic algorithms in mean-variance portfolio optimization

Can Berk Kalayci, Okkes Ertenlice, Hasan Akyer, Hakan Aygoren
2017 Pamukkale University Journal of Engineering Sciences  
Mean-variance portfolio optimization model, introduced by Markowitz, provides a fundamental answer to the problem of portfolio management.  ...  In this study, a literature review of genetic algorithms implementations on mean-variance portfolio optimization is examined from the recent published literature.  ...  (UCPO) Cardinality Constrained Portfolio Optimization (CCPO) Portfolio Optimization with Transaction Costs (POTC) Table 2 : 2 Mean-variance portfolio optimization problem specifications.  ... 
doi:10.5505/pajes.2017.37132 fatcat:m6xi6v6xarcavjbxsrlc7nhszi

A Comparative Study of Multi-objective Evolutionary Algorithms to Optimize the Selection of Investment Portfolios with Cardinality Constraints [chapter]

Feijoo E. Colomine Duran, Carlos Cotta, Antonio J. Fernández-Leiva
2012 Lecture Notes in Computer Science  
Two well-known performance measures are considered for this purpose: hypervolume and R2 indicator.  ...  This can be interpreted in terms of the exploration capabilities of the multi-objective optimizer for the richer (less-constrained) fitness landscapes.  ...  Figures 1-2 shows the distribution of values of the indicators for each algorithm for the two extreme cardinality values (K = 2 and K = 15).  ... 
doi:10.1007/978-3-642-29178-4_17 fatcat:6dhmauexafac5lvmwyixhcadwq
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