scholarly journals A Mating Selection Based on Modified Strengthened Dominance Relation for NSGA-III

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2837
Author(s):  
Saykat Dutta ◽  
Sri Srinivasa Raju M ◽  
Rammohan Mallipeddi ◽  
Kedar Nath Das ◽  
Dong-Gyu Lee
Keyword(s):  
Pareto Front ◽  
State Of The Art ◽  
Weight Vector ◽  
Dominance Relation ◽  
Additional Parameter ◽  
Pareto Dominance ◽  
Environmental Selection ◽  
Dominance Relationships ◽  
Test Suites ◽  
Selection Of

In multi/many-objective evolutionary algorithms (MOEAs), to alleviate the degraded convergence pressure of Pareto dominance with the increase in the number of objectives, numerous modified dominance relationships were proposed. Recently, the strengthened dominance relation (SDR) has been proposed, where the dominance area of a solution is determined by convergence degree and niche size (θ¯). Later, in controlled SDR (CSDR), θ¯ and an additional parameter (k) associated with the convergence degree are dynamically adjusted depending on the iteration count. Depending on the problem characteristics and the distribution of the current population, different situations require different values of k, rendering the linear reduction of k based on the generation count ineffective. This is because a particular value of k is expected to bias the dominance relationship towards a particular region on the Pareto front (PF). In addition, due to the same reason, using SDR or CSDR in the environmental selection cannot preserve the diversity of solutions required to cover the entire PF. Therefore, we propose an MOEA, referred to as NSGA-III*, where (1) a modified SDR (MSDR)-based mating selection with an adaptive ensemble of parameter k would prioritize parents from specific sections of the PF depending on k, and (2) the traditional weight vector and non-dominated sorting-based environmental selection of NSGA-III would protect the solutions corresponding to the entire PF. The performance of NSGA-III* is favourably compared with state-of-the-art MOEAs on DTLZ and WFG test suites with up to 10 objectives.

scholarly journals Hybrid Selection Based Multi/Many-Objective Evolutionary Algorithm

2021 ◽  
Author(s):  
Saykat Dutta ◽  
Rammohan Mallipeddi ◽  
Kedar Nath Das
Keyword(s):  
Pareto Front ◽  
State Of The Art ◽  
Superior Performance ◽  
Selection Strategy ◽  
Convergence Properties ◽  
Pareto Dominance ◽  
Advantages And Disadvantages ◽  
Environmental Selection ◽  
Hybrid Selection ◽  
Test Suites

Abstract In the last decade, numerous Multi/Many-Objective Evolutionary Algorithms (MOEAs) have been proposed to handle Multi/Many-Objective Problems (MOPs) with challenges such as discontinuous Pareto Front (PF), degenerate PF, etc. MOEAs in the literature can be broadly divided into three categories based on the selection strategy employed such as dominance, decomposition, and indicator-based MOEAs. Each category of MOEAs have their advantages and disadvantages when solving MOPs with diverse characteristics. In this work, we propose a Hybrid Selection based MOEA, referred to as HS-MOEA, which is a simple yet effective hybridization of dominance, decomposition and indicator-based concepts. In other words, we propose a new environmental selection strategy where the Pareto-dominance, reference vectors and an indicator are combined to effectively balance the diversity and convergence properties of MOEA during the evolution. The superior performance of HS-MOEA compared to the state-of-the-art MOEAs is demonstrated through experimental simulations on DTLZ and WFG test suites with up to 10 objectives.

scholarly journals Evolutionary Algorithm for Multiobjective Optimization Based on Density Estimation Ranking

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Lin Li ◽  
Hengfei Wu ◽  
Xiujian Hu ◽  
Guanglei Sheng
Keyword(s):  
Evolutionary Algorithms ◽  
Multiobjective Optimization ◽  
Density Estimation ◽  
Pareto Front ◽  
Selection Criterion ◽  
Pareto Dominance ◽  
Environmental Selection ◽  
Different Shapes ◽  
Pareto Fronts ◽  
Optimal Candidate

In the past few decades, a number of multiobjective evolutionary algorithms (MOEAs) have been proposed in the continue study. As pointed out in some recent studies, the performance of the most existing MOEAs is not promising when solving different shapes of Pareto fronts. To address this issue, this paper proposes an MOEA based on density estimation ranking. The algorithm includes density estimation ranking to shift the reference solution position, calculating the density of candidate solutions and ranking by the estimated density value, to modify the Pareto dominance relation and for handling complicated Pareto front. The result of this ranking can be used as the second selection criterion for environmental selection, and the optimal candidate individual with distribution and diversity information is selected. Experimental results show that the proposed algorithm can solve various types of Pareto fronts, outperformance several state-of-the-art evolutionary algorithms in multiobjective optimization.

scholarly journals Sensitivity Based Selection of an Optimal Cable-Driven Parallel Robot Design for Rehabilitation Purposes

Robotics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Ferdaws Ennaiem ◽  
Abdelbadiâ Chaker ◽  
Juan Sebastián Sandoval Arévalo ◽  
Med Amine Laribi ◽  
Sami Bennour ◽  
...  
Keyword(s):  
Pareto Front ◽  
Parallel Robot ◽  
Elastic Stiffness ◽  
Upper Limb Rehabilitation ◽  
Daily Life Activities ◽  
System A ◽  
The Monte Carlo Method ◽  
Genetic Algorithm Method ◽  
Limb Rehabilitation ◽  
Selection Of

This paper deals with the design of an optimal cable-driven parallel robot (CDPR) for upper limb rehabilitation. The robot’s prescribed workspace is identified with the help of an occupational therapist based on three selected daily life activities, which are tracked using a Qualisys motion capture system. A preliminary architecture of the robot is proposed based on the analysis of the tracked trajectories of all the activities. A multi-objective optimization process using the genetic algorithm method is then performed, where the cable tensions and the robot size are selected as the objective functions to be minimized. The cables tensions are bounded between two limits, where the lower limit ensures a positive tension in the cables at all times and the upper limit represents the maximum torque of the motor. A sensitivity analysis is then performed using the Monte Carlo method to yield the optimal design selected out of the non-dominated solutions, forming the obtained Pareto front. The robot with the highest robustness toward the disturbances is identified, and its dexterity and elastic stiffness are calculated to investigate its performance.

Interpreting testing and assessment: A state-of-the-art review

2021 ◽  
pp. 026553222110361
Author(s):  
Chao Han
Keyword(s):  
State Of The Art ◽  
Professional Certification ◽  
Automatic Assessment ◽  
Prospective Students ◽  
Assessment Practice ◽  
High Stakes ◽  
Future Directions ◽  
Assessment Tasks ◽  
Interpreter Education ◽  
Selection Of

Over the past decade, testing and assessing spoken-language interpreting has garnered an increasing amount of attention from stakeholders in interpreter education, professional certification, and interpreting research. This is because in these fields assessment results provide a critical evidential basis for high-stakes decisions, such as the selection of prospective students, the certification of interpreters, and the confirmation/refutation of research hypotheses. However, few reviews exist providing a comprehensive mapping of relevant practice and research. The present article therefore aims to offer a state-of-the-art review, summarizing the existing literature and discovering potential lacunae. In particular, the article first provides an overview of interpreting ability/competence and relevant research, followed by main testing and assessment practice (e.g., assessment tasks, assessment criteria, scoring methods, specificities of scoring operationalization), with a focus on operational diversity and psychometric properties. Second, the review describes a limited yet steadily growing body of empirical research that examines rater-mediated interpreting assessment, and casts light on automatic assessment as an emerging research topic. Third, the review discusses epistemological, psychometric, and practical challenges facing interpreting testers. Finally, it identifies future directions that could address the challenges arising from fast-changing pedagogical, educational, and professional landscapes.

Monopoly Regulation Under Relaxed Pareto Efficiency

2017 ◽  
Vol 5 (2) ◽  
pp. 162-176
Author(s):  
Ismail Saglam
Keyword(s):  
Marginal Cost ◽  
Regulatory Environment ◽  
Pareto Dominance ◽  
Incentive Compatible ◽  
Monopoly Regulation ◽  
Dominance Relationships ◽  
Ex Post ◽  
Ex Ante ◽  
Pareto Axiom ◽  
Set Of Points

Baron and Myerson (BM; 1982, Econometrica, 50(4), 911–930) propose an incentive-compatible, individually rational and ex ante socially optimal direct-revelation mechanism to regulate a monopolistic firm with unknown costs. Their mechanism is not ex post Pareto dominated by any other feasible direct-revelation mechanism. However, there also exist an uncountable number of feasible direct-revelation mechanisms that are not ex post Pareto dominated by the BM mechanism. To investigate whether the BM mechanism remains in the set of ex post undominated mechanisms when the Pareto axiom is slightly weakened, we introduce the ∈-Pareto dominance. This concept requires the relevant dominance relationships to hold in the support of the regulator’s beliefs everywhere except for a set of points of measure ∈, which can be arbitrarily small. We show that a modification of the BM mechanism which always equates the price to the marginal cost can ∈-Pareto dominate the BM mechanism at uncountably many regulatory environments, while it is never ∈-Pareto dominated by the BM mechanism at any regulatory environment.

scholarly journals A look-ahead Monte Carlo simulation method for improving parental selection in trait introgression

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saba Moeinizade ◽  
Ye Han ◽  
Hieu Pham ◽  
Guiping Hu ◽  
Lizhi Wang
Keyword(s):  
Monte Carlo ◽  
State Of The Art ◽  
Simulation Method ◽  
Multiple Trait ◽  
Look Ahead ◽  
Probability Of Success ◽  
Genetic Distribution ◽  
Selection Of ◽  
Trait Introgression

AbstractMultiple trait introgression is the process by which multiple desirable traits are converted from a donor to a recipient cultivar through backcrossing and selfing. The goal of this procedure is to recover all the attributes of the recipient cultivar, with the addition of the specified desirable traits. A crucial step in this process is the selection of parents to form new crosses. In this study, we propose a new selection approach that estimates the genetic distribution of the progeny of backcrosses after multiple generations using information of recombination events. Our objective is to select the most promising individuals for further backcrossing or selfing. To demonstrate the effectiveness of the proposed method, a case study has been conducted using maize data where our method is compared with state-of-the-art approaches. Simulation results suggest that the proposed method, look-ahead Monte Carlo, achieves higher probability of success than existing approaches. Our proposed selection method can assist breeders to efficiently design trait introgression projects.

Many-objective optimization algorithm based on adaptive reference vector

2021 ◽  
Vol 40 (1) ◽  
pp. 449-461
Author(s):  
Ziyu Hu ◽  
Xuemin Ma ◽  
Hao Sun ◽  
Jingming Yang ◽  
Zhiwei Zhao
Keyword(s):  
Selection Pressure ◽  
Test Problem ◽  
Pareto Front ◽  
Local Area ◽  
Reference Vector ◽  
Selection Mechanism ◽  
Multi Objective Optimization ◽  
Minimum Standard ◽  
Pareto Dominance ◽  
State Of Art

When dealing with multi-objective optimization, the proportion of non-dominated solutions increase rapidly with the increase of optimization objective. Pareto-dominance-based algorithms suffer the low selection pressure towards the true Pareto front. Decomposition-based algorithms may fail to solve the problems with highly irregular Pareto front. Based on the analysis of the two selection mechanism, a dynamic reference-vector-based many-objective evolutionary algorithm(RMaEA) is proposed. Adaptive-adjusted reference vector is used to improve the distribution of the algorithm in global area, and the improved non-dominated relationship is used to improve the convergence in a certain local area. Compared with four state-of-art algorithms on DTLZ benchmark with 5-, 10- and 15-objective, the proposed algorithm obtains 13 minimum mean IGD values and 8 minimum standard deviations among 15 test problem.

scholarly journals State of the art in selection of variables and functional forms in multivariable analysis—outstanding issues

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Willi Sauerbrei ◽  
◽  
Aris Perperoglou ◽  
Matthias Schmid ◽  
Michal Abrahamowicz ◽  
...  
Keyword(s):  
State Of The Art ◽  
Multivariable Analysis ◽  
Selection Of Variables ◽  
Functional Forms ◽  
Selection Of

scholarly journals Identifying Informatively Easy and Informatively Hard Concepts

2022 ◽  
Vol 22 (1) ◽  
pp. 1-28
Author(s):  
R. Paul Wiegand ◽  
Anthony Bucci ◽  
Amruth N. Kumar ◽  
Jennifer Albert ◽  
Alessio Gaspar
Keyword(s):  
Performance Measures ◽  
Item Analysis ◽  
Scale Up ◽  
Concept Inventory ◽  
Pareto Dominance ◽  
Dominance Relationships ◽  
Coevolutionary Computation ◽  
Introductory Computer Programming ◽  
Student Problem ◽  
Online Software

In this article, we leverage ideas from the theory of coevolutionary computation to analyze interactions of students with problems. We introduce the idea of informatively easy or hard concepts. Our approach is different from more traditional analyses of problem difficulty such as item analysis in the sense that we consider Pareto dominance relationships within the multidimensional structure of student–problem performance data rather than average performance measures. This method allows us to uncover not just the problems on which students are struggling but also the variety of difficulties different students face. Our approach is to apply methods from the Dimension Extraction Coevolutionary Algorithm to analyze problem-solving logs of students generated when they use an online software tutoring suite for introductory computer programming called problets . The results of our analysis not only have implications for how to scale up and improve adaptive tutoring software but also have the promise of contributing to the identification of common misconceptions held by students and thus, eventually, to the construction of a concept inventory for introductory programming.

scholarly journals A recommendations model with multiaspect awareness and hierarchical user-product attention mechanisms

2020 ◽  
Vol 17 (3) ◽  
pp. 849-865
Author(s):  
Zhongqin Bi ◽  
Shuming Dou ◽  
Zhe Liu ◽  
Yongbin Li
Keyword(s):  
State Of The Art ◽  
Weight Vector ◽  
User Preferences ◽  
The Other ◽  
Attention Networks ◽  
Proposed Model ◽  
Network Methods ◽  
Public Datasets ◽  
Novel Model ◽  
Attention Weight

Neural network methods have been trained to satisfactorily learn user/product representations from textual reviews. A representation can be considered as a multiaspect attention weight vector. However, in several existing methods, it is assumed that the user representation remains unchanged even when the user interacts with products having diverse characteristics, which leads to inaccurate recommendations. To overcome this limitation, this paper proposes a novel model to capture the varying attention of a user for different products by using a multilayer attention framework. First, two individual hierarchical attention networks are used to encode the users and products to learn the user preferences and product characteristics from review texts. Then, we design an attention network to reflect the adaptive change in the user preferences for each aspect of the targeted product in terms of the rating and review. The results of experiments performed on three public datasets demonstrate that the proposed model notably outperforms the other state-of-the-art baselines, thereby validating the effectiveness of the proposed approach.

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