Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization

Quintana, David; Denysiuk, Roman; García-Rodríguez, Sandra; Gaspar‐Cunha, A.

doi:10.3390/app7101079

Cited by 11 publications

(7 citation statements)

References 43 publications

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“…In particular, we believe that the possibility to increase the size of the neural network across generations through a mechanism analogous to that used by NEAT [13] and by SUNA [66] and weight decay mechanisms preventing an excessive growth of parameters might lead to even higher performance. Another interesting aspect deserving further investigation in future research is constituted by behavioral plasticity defined as the ability of agents to display multiple behavioral responses which might differ in a continuous or discontinuous way, in a condition-sensitive manner [5, 67]. Indeed, the ability to display an articulated behavioral repertoire combined with the ability to select immediately the behavior appropriate to the current perceived circumstances can support the synthesis of highly robust solutions.…”

Section: Discussionmentioning

confidence: 99%

“…The evolution of behavioral plastic agents can be promoted through the evolution of modular controllers [68–69] and the inclusion of mechanisms facilitating the perception of behavioural affordances, i.e. the perception of states eliciting the execution of specific behavioral responses [5].…”

Section: Discussionmentioning

confidence: 99%

“…We decided to use agents situated in an external environment since it permits to study the effect of environmental and agent/environmental variations. For examples of other possible application domains see [5–7]. Moreover, we decided to use neuro-controlled agents to exploit the ability of neural networks to regulate their output on the basis of their input and generalize, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Robust optimization through neuroevolution

Pagliuca

Nolfi

2019

PLoS ONE

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We propose a method for evolving neural network controllers robust with respect to variations of the environmental conditions (i.e. that can operate effectively in new conditions immediately, without the need to adapt to variations). The method specifies how the fitness of candidate solutions can be evaluated, how the environmental conditions should vary during the course of the evolutionary process, which algorithm can be used, and how the best solution can be identified. The obtained results show how the method proposed is effective and computational tractable. It allows to improve performance on an extended version of the double-pole balancing problem, outperform the best available human-designed controllers on a car racing problem, and generate effective solutions for a swarm robotic problem. The comparison of different algorithms indicates that the CMA-ES and xNES methods, that operate by optimizing a distribution of parameters, represent the best options for the evolution of robust neural network controllers.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust optimization through neuroevolution

Pagliuca

Nolfi

2019

PLoS ONE

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“…Quintana et al [ 20 ], also present another variation of SMS-EMOA, a robustness-based S-metric selection evolutionary multiobjective optimization algorithm (R-SMS-EMOA) for robust portfolio optimization. This algorithm adjusts the optimization process, focusing it on the most stable regions of the search space to mitigate portfolio implementation risk.…”

Section: Introductionmentioning

confidence: 99%

Resampled Efficient Frontier Integration for MOEAs

Quintana

Moreno

2021

Entropy

Self Cite

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Mean-variance portfolio optimization is subject to estimation errors for asset returns and covariances. The search for robust solutions has been traditionally tackled using resampling strategies that offer alternatives to reference sets of returns or risk aversion parameters, which are subsequently combined. The issue with the standard method of averaging the composition of the portfolios for the same risk aversion is that, under real-world conditions, the approach might result in unfeasible solutions. In case the efficient frontiers for the different scenarios are identified using multiobjective evolutionary algorithms, it is often the case that the approach to averaging the portfolio composition cannot be used, due to differences in the number of portfolios or their spacing along the Pareto front. In this study, we introduce three alternatives to solving this problem, making resampling with standard multiobjective evolutionary algorithms under real-world constraints possible. The robustness of these approaches is experimentally tested on 15 years of market data.

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“…Robust models are also widely studied and applied in project portfolio problems to solve the difficulties in determining probabilities of future scenarios, aiming to select an ideal system portfolio that performances well at almost all possible situations [14][15][16]. As for the evaluation and trade-off of project portfolios, variant methods are proposed and studied, such as risk analysis methods [17], value evaluation methods [18], cost-efficiency methods [19], fuzzy assessment methods [4], preference-based methods [20], game theory, interactive decision methods [21], etc. A common ground of those methods is determining the value and risk of a system portfolio to abstractly indicate what decision-makers expect or not expect.…”

Section: Introductionmentioning

confidence: 99%

Model and Data-Driven System Portfolio Selection Based on Value and Risk

et al. 2019

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System portfolio selection is a kind of tradeoff analysis and decision-making on multiple systems as a whole to fulfill the overall performance on the perspective of System of Systems (SoS). To avoid the subjectivity of traditional expert experience-dependent models, a model and data-driven approach is proposed to make an advance on the system portfolio selection. Two criteria of value and risk are used to indicate the quality of system portfolios. A capability gap model is employed to determine the value of system portfolios, with the weight information determined by correlation analysis. Then, the risk is represented by the remaining useful life (RUL), which is predicted by analyzing time series of system operational data. Next, based on the value and risk, an optimization model is proposed. Finally, a case with 100 candidate systems is studied under the scenario of anti-missile. By utilizing the Non-dominated Sorting Differential Evolution (NSDE) algorithm, a Pareto set with 200 individuals is obtained. Some characters of the Pareto set are analyzed by discussing the frequency of being selected and the association rules. Through the conclusion of the whole procedures, it can be proved that the proposed model and data-driven approach is feasible and effective for system portfolio selection.

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Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization

Cited by 11 publications

References 43 publications

Robust optimization through neuroevolution

Robust optimization through neuroevolution

Resampled Efficient Frontier Integration for MOEAs

Model and Data-Driven System Portfolio Selection Based on Value and Risk

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