Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

Karshenas, Hossein; Santana, Roberto; Bielza, Concha; Larrañaga, Pedro

doi:10.1109/tevc.2013.2281524

Cited by 90 publications

(41 citation statements)

References 80 publications

(39 reference statements)

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“…Finally, it generates an offspring population by selecting superior individuals from the united population formed based on the father population and the temporary population. EDA has been successfully 50 applied in various fields [22,39,45].…”

Section: Problemmentioning

confidence: 99%

Gesture segmentation based on a two-phase estimation of distribution algorithm

Liu

Gong

Meng

et al. 2017

Information Sciences

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract A multi-objective optimization model for the problem of gesture segmentation is formulated, and a method of solving the model based on a two-phase estimation of distribution algorithm is presented. When building the model, the positions of a series of pixels are taken as the decision variable, and the differences between the colors of pixels and those of a hand are taken as objective functions.A method of gesture segmentation based on a two-phase estimation of distribution algorithm is proposed according to the correlation among the positions of pixels. The method divides the solution of the problem based on evolutionary optimization into two phases, and uses different estimation of distribution algorithms in different phases. In the first phase, the probability model of candidates is formulated by a number of intervals given the fact that the positions of hand pixels distribute in several intervals. In the second phase, the probability model of candidates is built through a series of segments since the positions of *Manuscript (including abstract) Click here to view linked References hand pixels further distribute around curves. A series of pixels constituting a hand region are obtained based on sampling by the above probability models.The proposed method is applied to 2515 problems of gesture segmentation, and is compared with the existing methods. The experimental results demonstrate the effectiveness of the proposed method.

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Section: Problemmentioning

confidence: 99%

Gesture segmentation based on a two-phase estimation of distribution algorithm

Liu

Gong

Meng

et al. 2017

Information Sciences

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“…To the best of our knowledge, the only MOEDA work that has addressed this issue is related to the use of the multi-objective hierarchical BOA (mhBOA) [107,109]. An interesting work in this direction is put forward in [68] where a MOEDA applies a multidimensional Bayesian network as its probabilistic model in order to, it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learned between variables in other Bayesian network-based EDAs.…”

Section: Final Remarks and Salient Issuesmentioning

confidence: 99%

MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm

et al. 2016

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The extension of estimation of distribution algorithms (EDAs) to the multiobjective domain has led to multi-objective optimization EDAs (MOEDAs). Most MOEDAs have limited themselves to porting single-objective EDAs to the multi-objective domain. Although MOEDAs have proved to be a valid approach, the last point is an obstacle to the achievement of a significant improvement regarding "standard" multi-objective optimization evolutionary algorithms. Adapting the model-building algorithm is one way to achieve a substantial advance. Most model-building schemes used so far by EDAs employ off-the-shelf machine learning methods. However, the model-building problem has particular requirements that those methods do not meet and even evade. The focus of this paper is on the modelbuilding issue and how it has not been properly understood and addressed by most MOEDAs. We delve down into the roots of this matter and hypothesize about its causes. To gain a deeper understanding of the subject we propose a novel algorithm intended to overcome the drawbacks of current MOEDAs. This new algorithm is the multi-objective neural estimation of distribution algorithm (MONEDA). MONEDA uses a modified growing neural gas network for model-building (MB-GNG). MB-GNG is a custom-made clustering algorithm that meets the above demands. Thanks to its custom-made model-building algorithm, the preservation of elite individuals and its individual replacement scheme, MONEDA is capable of scalably B Luis Martí 123 J Glob Optim solving continuous multi-objective optimization problems. It performs better than similar algorithms in terms of a set of quality indicators and computational resource requirements.

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“…The method can also be extended to the case where only some of the objec tive functions of the MOP are noisy. This solution ranking method is then integrated into a standard multi-objective EA and MBN-EDA [14], a multi-objective EDA based on joint variable-objective probabilistic modeling, for finding the solutions of noisy MOPs.…”

Section: Wfj E T Fj(x) < Fj(y) and 2 3/fc E T Fk(x)< Fk(y)mentioning

confidence: 99%

“…A multi-objective EDA based on this idea is MBN-EDA [14], which uses multidimensional Bayesian networks (MBNs), a type of Bayesian networks [40] initially used in multi-dimensional classification [41,42], for joint modeling of variables and objectives. Figure 2 shows an example of an MBN structure.…”

Section: Multi-objective Optimization With Joint Variable-objective Pmentioning

confidence: 99%

Interval-based ranking in noisy evolutionary multi-objective optimization

2014

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As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many realworld multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling noisy objectives in evolutionary multi-objective optimization algorithms becomes very important and is gaining more attention in recent years. In this paper we present α-degree Pareto dominance relation for ordering the solutions in multi-objective optimization when the values of the objective functions are given as intervals. Based on this dominance relation, we propose an adaptation of the non-dominated sorting algorithm for ranking the solutions. This ranking method is then used in a standard multi-objective evolution ary algorithm and a recently proposed novel multi-objective estimation of distribution algorithm based on joint variable-objective probabilistic modeling, and applied to a set of multi-objective problems with different levels of independent noise. The experi mental results show that the use of the proposed method for solution ranking allows to approximate Pareto sets which are considerably better than those obtained when using the dominance probability-based ranking method, which is one of the main methods for noise handling in multi-objective optimization.

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Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

Cited by 90 publications

References 80 publications

Gesture segmentation based on a two-phase estimation of distribution algorithm

Gesture segmentation based on a two-phase estimation of distribution algorithm

MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm

Interval-based ranking in noisy evolutionary multi-objective optimization

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