Portfolio selection problem: New multicriteria approach for the mean-semivariance model

Senhaji, Kaoutar; Moutaouakil, Karim El; Ettaouil, Mohamed

doi:10.1109/gol.2016.7731711

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Therefore, the above parameter setting according to the implicit parameters C φ = {α, φ 1 , ε} [29]:…”

Section: H) Portfolio Selection Problem (Psp)mentioning

confidence: 99%

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On Hyperparameters Optimization of the Continuous Hopfield Network via Genetic Algorithm

Safae,

Haddouch,

MOUTAOUAKIL

2023

Preprint

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Recurrent neural networks have proven to be effective in various domains due to their ability to remember key information during solution processes. The continuous Hopfield network is a recurrent neural network that can solve several complicated problems, including combinatorial optimization problems. The user of a continuous Hopfield network for solving combinatorial optimization problems must construct an energy function that combines the energy function and the constraints; this combination requires penalty hyper-parameters that have a direct impact on the quality of the solution and its feasibility. To ensure the convergence of the continuous Hopfield network and thus ensure the feasibility of the solutions to the combinatorial problems, we introduce a linear optimization model given by: the objective function represents the energy function of continuous Hopfield network, which controls the quality of the solution, and the constraints represent conditions on the parameters of the continuous Hopfield network penalty function extracted by the hyperplane procedure, which ensure the feasibility of the solutions. On the well-known NP-complete problem (task assignment problem, traveling salesman problem, weighted constraint satisfaction problem, max-stable problem, graph coloring problem, shortest path problem and portfolio selection problem) the genetic algorithm is used to solve the proposed model. The proposed method has shown its superiority over random methods for choosing continuous Hopfield network hyper-parameters: the solutions produced are all feasible, and the difference in accuracy between our method and random methods is 48.8%.

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“…Therefore, the above parameter setting according to the implicit parameters C φ = {α, φ 1 , ε} [29]:…”

Section: H) Portfolio Selection Problem (Psp)mentioning

confidence: 99%

“…Comparing the PSP with the energy function Eq. ( 7) of the Hopfield model, it can be translated into [29,30]…”

Section: H) Portfolio Selection Problem (Psp)mentioning

confidence: 99%

On Hyperparameters Optimization of the Continuous Hopfield Network via Genetic Algorithm

Safae,

Haddouch,

MOUTAOUAKIL

2023

Preprint

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“…Numerical experiments indicate that NSGA-II outperforms SPEA 2 in-sample. Senhaji et al (2016) propose to resolve the problem by combining the continuous Hopfield neural network with NSGA-II. The effectiveness of this strategy is proved using a portfolio of 20 assets.…”

Section: Introductionmentioning

confidence: 99%

Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures

2019

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In this study, we analyze three portfolio selection strategies for loss-averse investors: semi-variance, conditional value-at-risk, and a combination of both risk measures. Moreover, we propose a novel version of the non-dominated sorting genetic algorithm II and of the strength Pareto evolutionary algorithm 2 to tackle this optimization problem. The effectiveness of these algorithms is compared with two alternatives from the literature from five publicly available datasets. The computational results indicate that the proposed algorithms in this study outperform the others for all the examined performance metrics. Moreover, they are able to approximate the Pareto front even in cases in which all the other approaches fail.

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Optimizing hyperparameters in Hopfield neural networks using evolutionary search

Rbihou,

Haddouch,

El moutaouakil

2024

OPSEARCH

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Portfolio selection problem: New multicriteria approach for the mean-semivariance model

Cited by 6 publications

References 15 publications

On Hyperparameters Optimization of the Continuous Hopfield Network via Genetic Algorithm

On Hyperparameters Optimization of the Continuous Hopfield Network via Genetic Algorithm

Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures

Optimizing hyperparameters in Hopfield neural networks using evolutionary search

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