A Portfolio Optimization Approach to Selection in Multiobjective Evolutionary Algorithms

Yevseyeva, Iryna; Guerreiro, Andreia P.; Emmerich, Michael; Fonseca, Carlos M.

doi:10.1007/978-3-319-10762-2_66

Cited by 28 publications

(16 citation statements)

References 12 publications

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“…Hence, the selection procedure should simultaneously optimize quality and diversity of population. In [29], a multiobjective evolutionary algorithm based on the portfolio selection idea was introduced and results comparable to the results of the state-of-the-art algorithms were obtained.…”

Section: Portfolio Selection As a Multi-objective Optimization Problemmentioning

confidence: 92%

“…Recently, the principles of portfolio optimization have been successfully applied not only for optimizing financial portfolio selection [24], but also in other domains, such as strategic decision making [14] (for instance, team management), projects selection [11], IT project portfolio management [3], and evolutionary algorithms selection [29]. For instance, for evolutionary algorithms selection it is important to keep good, but different individuals, which should avoid fast convergence of the population to a single individual or few similar individuals.…”

Section: Portfolio Selection As a Multi-objective Optimization Problemmentioning

confidence: 99%

“…The MOEA for the portfolio subset selection problems represents individuals (that are portfolios) as sorted index lists. For instance, the sequence(1,4,6,29) represents the portfolio that selects the 1st, the 4th, the 6th and the 29th molecules.The mutation is done by (1) deleting a single randomly chosen molecule from the portfolio, (2) adding a randomly chosen new molecule, and (3) replacing a molecule inside the portfolio by a molecule outside the portfolio. Each of these mutation operators is applied with a certain probability for each molecule, which is denoted by p M D , p M A , and respectively p M R .…”

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confidence: 99%

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Application of portfolio optimization to drug discovery

Yevseyeva

Lenselink

Vries

et al. 2019

Information Sciences

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In this work, a problem of selecting a subset of molecules, which are potential lead candidates for drug discovery, is considered. Such molecule subset selection problem is formulated as a portfolio optimization, well known and studied in financial management. The financial return, more precisely the return rate, is interpreted as return rate from a potential lead and calculated as a product of gain and probability of success (probability that a selected molecule becomes a lead), which is related to performance of the molecule, in particular, its (bio-)activity. The risk is associated with not finding active molecules and is related to the level of diversity of the molecules selected in portfolio. It is due to potential of some molecules to contribute to the diversity of the set of molecules selected in portfolio and hence decreasing risk of portfolio as a whole. Even though such molecules considered in isolation look inefficient, they are located in sparsely sampled regions of chemical space and are different from more promising molecules. One way of computing diversity

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Section: Portfolio Selection As a Multi-objective Optimization Problemmentioning

confidence: 92%

Section: Portfolio Selection As a Multi-objective Optimization Problemmentioning

confidence: 99%

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confidence: 99%

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Application of portfolio optimization to drug discovery

Yevseyeva

Lenselink

Vries

et al. 2019

Information Sciences

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“…The POSEA is also an interesting indicator based MOEA developed by Yevseyeva et al in [23], where the Sharpe-Ratio indicator was proposed based on a formulation of fitness assignment as a portfolio selection problem. In POSEA, the Sharpe-Ratio indicator is combined with the hypervolume indicator, which could provide an alternative way to generalize hypervolume to many-objective optimization.…”

Section: A Indicator Based Moeasmentioning

confidence: 99%

“…The HypE proposed in [22] is also a hypervolume indicator based MOEA, where the Monter Carlo simulation is adopted to estimate the hypervolume contributions of the candidate solutions for addressing the high complexity of exact hypervolume calculation in solving MaOPs. It is worth noting that the recently proposed weakly Pareto compliant Sharpe-Ratio indicator also provides an alternative way to generalize hypervolume to manyobjective optimization [23], [24].…”

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confidence: 99%

An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility

Tian

Cheng

Zhang

et al. 2018

IEEE Trans. Evol. Computat.

545

157

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Abstract-During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, however, the performance of an MOEA can strongly depend on the Pareto front shape of the problem to be solved, whereas most existing MOEAs show poor versatility on problems with different shapes of Pareto fronts. To address this issue, we propose an MOEA based on an enhanced inverted generational distance indicator, in which an adaptation method is suggested to adjust a set of reference points based on the indicator contributions of candidate solutions in an external archive. Our experimental results demonstrate that the proposed algorithm is versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multi-objective and many-objective optimization.

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Randomized time‐varying knapsack problems via binary beetle antennae search algorithm: Emphasis on applications in portfolio insurance

Medvedeva

Katsikis

Mourtas

et al. 2020

Math Methods in App Sciences

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The knapsack problem is a problem in combinatorial optimization, and in many such problems, exhaustive search is not tractable. In this paper, we describe and analyze the randomized time-varying knapsack problem (RTVKP) as a time-varying integer linear programming (TV-ILP) problem. In this way, we present the on-line solution to the RTVKP combinatorial optimization problem and highlight the restrictions of static methods. In addition, the RTVKP is applied in the field of finance and converted into a portfolio insurance problem. Our methodology is confirmed by simulation tests in real-world data sets, in order to explain being an excellent alternative to traditional approaches.

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A Portfolio Optimization Approach to Selection in Multiobjective Evolutionary Algorithms

Cited by 28 publications

References 12 publications

Application of portfolio optimization to drug discovery

Application of portfolio optimization to drug discovery

An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility

Randomized time‐varying knapsack problems via binary beetle antennae search algorithm: Emphasis on applications in portfolio insurance

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