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IEEE Congress on evolutionary Computation (CEC)

作者： Seah, Chun-Wei Ong, Yew-Soon Tsang, Ivor W. Jiang, Siwei Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

ISBN: (纸本)9781467315098

In this paper, the interest is on cases where assessing the goodness of a solution for the problem is costly or hazardous to construct or extremely computationally intensive to compute. We label such category of problems as "expensive" in the present study. In the context of multi-objective evolutionary optimizations, the challenge amplifies, since multiple criteria assessments, each defined by an "expensive" objective is necessary and it is desirable to obtain the Pareto-optimal solution set under a limited resource budget. To address this issue, we propose a Pareto Rank Learning scheme that predicts the Pareto front rank of the offspring in MOEAs, in place of the "expensive" objectives when assessing the population of solutions. Experimental study on 19 standard multi-objective benchmark test problems concludes that Pareto rank learning enhanced MOEA led to significant speedup over the state-of-the-art NSGA-II, MOEA/D and SPEA2.

关键词： multi-objective evolutionary algorithms Expensive Problems Pareto Rank Learning

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Applying Social Choice Theory to Solve Engineering multi-objective Optimization Problems

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2020年第1期31卷 119-128页

作者： de Carvalho, Vinicius Renan Larson, Kate Brandao, Anarosa Alves Franco Sichman, Jaime Simao Univ Sao Paulo EP LTI Sao Paulo Brazil Univ Waterloo Cheriton Sch Comp Sci Waterloo ON Canada

multi-objective optimization problems usually do not have a single unique optimal solution, for either discrete or continuous domains. Furthermore, there are usually many possible available algorithms for solving these problems, and one typically does not know in advance which of these will be the most effective for solving a particular problem instance. Hyper-heuristics (HHs) are often used as a means to make this choice. In particular, the underlying idea of HHs is to run several algorithms or heuristics and dynamically decide, based on different criteria, which problem or part of the problem should be solved by which algorithm or heuristic. On the other hand, the domain of social choice theory studies how to design collective decision processes by aggregating individual inputs into collective ones. In this paper, we explore the use of social choice theory in creating HHs. By using HHs based on different voting methods, like Borda, Copeland and Kemeny-Young, we show how we can solve both continuous and discrete engineering multi-objective optimization problems and discuss the results obtained by each of these methods. Our obtained results show that our strategy has found solutions that are at least equals to the ones generated by the best algorithm among the studied ones, and sometimes even overcomes these results.

关键词： Hyper-heuristics multi-objective evolutionary algorithms Voting methods Crashworthiness Car side impact Machining Water resource planning multi-objective travel salesperson problem

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A novel multi-objective evolutionary algorithm based on subpopulations for the bi-objective traveling salesman problem

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SOFT COMPUTING 2019年第15期23卷 6157-6168页

作者： Moraes, Deyvid Heric Sanches, Danilo Sipoli Rocha, Josimar da Silva Caldonazzo Garbelini, Jader Maikol Castoldi, Marcelo Favoretto Univ Tecnol Fed Parana Av Alberto Carazzai Cornelio Procopio PR Brazil

This paper presents a new approach called MOEA/NSM (multi-objective evolutionary algorithm integrating NSGA-II, SPEA2 and MOEA/D features). This paper combines the main characteristics of the NSGA-II, SPEA2 and MOEA/D algorithms, and also including 2-opt local search technique to improve the objective space search. The MOEA/NSM algorithm was compared to the other classical approaches using 9 datasets for the bi-objective traveling salesman problem. In addition, experiments were carried out applying the local search in the classical approaches, resulting in a considerable improvement in the results for these algorithms. From the Pareto frontiers resulting from the experiments, we applied the evaluation metrics by hypervolume, Epsilon (E), EAF and Shapiro-Wilk statistical hypothesis test. The results showed a better performance of the MOEA/NSM when compared with NSGA-II, SPEA2 and MOEA/D.

关键词： multi-objective evolutionary algorithms Bi-objective traveling salesman problem NSGA-II MOEA D Local search

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A multi-objective evolutionary fuzzy system to obtain a broad and accurate set of solutions in intrusion detection systems

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SOFT COMPUTING 2019年第4期23卷 1321-1336页

作者： Elhag, Salma Fernandez, Alberto Altalhi, Abdulrahman Alshomrani, Saleh Herrera, Francisco Univ Jeddah Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia

Intrusion detection systems are devoted to monitor a network with aims at finding and avoiding anomalous events. In particular, we focus on misuse detection systems, which are trained to identify several known types of attacks. These can be unauthorized accesses, or denial of service attacks, among others. Whenever it scans a trace of a suspicious event, it is programmed to trigger an alert and/or to block this dangerous access to the system. Depending on the security policies of the network, the administrator may seek different requirements that will have a strong dependency on the behavior of the intrusion detection system. For a given application, the cost of raising false alarms could be higher than carrying out a preventive access lock. In other scenarios, there could be a necessity of correctly identifying the exact type of cyber attack to proceed in a given way. In this paper, we propose a multi-objective evolutionary fuzzy system for the development of a system that can be trained using different metrics. By increasing the search space during the optimization of the model, more accurate solutions are expected to be obtained. Additionally, this scheme allows the final user to decide, among a broad set of solutions, which one is better suited for the current network characteristics. Our experimental results, using the well-known KDDCup'99 problem, supports the quality of this novel approach in contrast to the state-of-the-art for evolutionary fuzzy systems in intrusion detection, as well as the C4.5 decision tree.

关键词： Intrusion detection systems Computational intelligence evolutionary fuzzy systems multi-objective evolutionary algorithms Misuse detection

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Performance, Energy, and Temperature Enabled Task Scheduling using evolutionary Techniques

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2019年 22卷 272-286页

作者： Sheikh, Hafiz Fahad Ahmad, Ishfaq Arshad, Sheheryar Ali Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

In allocating parallel tasks to cores, most energy and thermal-aware scheduling techniques rely on Dynamic Voltage and Frequency Scaling (DVFS) to mark up and down core speeds for running the system under the desired constraints. While these techniques often meet the imposed system constraints, they are maladroit at identifying the best possible trade-offs between performance and energy, or between performance and temperature. This paper focuses on task-to-core allocation for optimizing performance (P), energy (E), and temperature (T) simultaneously. A solution set yielded by such algorithm comprises of multiple points forming a Pareto-front, not just scalar values. This paper employs Strength Pareto evolutionary Algorithm (SPEA) and Non-Dominated Sorting Genetic Algorithm (NSGA), which have been demonstrated to be superior evolutionary optimization approaches in several domains. The paper utilizes and compares these techniques in DVFS-based PET-enabled scheduling algorithms, and highlights the differences between the two approaches. The paper also explores how the algorithmic characteristics affect the performance of the scheduling schemes. A variety of criteria combined with extensive experimentation help to compare the two approaches. The results show how varying different system and task parameters affect not just the PET goals individually and collectively but also the quality of trade-offs as well as the spread of solutions on the Pareto-front. (C) 2017 Published by Elsevier Inc.

关键词： Task scheduling Dynamic thermal management multi-objective evolutionary algorithms multi-core systems DAG scheduling

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A methodology for evaluating multi-objective evolutionary feature selection for classification in the context of virtual screening

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SOFT COMPUTING 2019年第18期23卷 8775-8800页

作者： Jimenez, Fernando Perez-Sanchez, Horacio Palma, Jose Sanchez, Gracia Martinez, Carlos Univ Murcia Fac Informat Dept Informat & Commun Engn E-30100 Murcia Spain Catholic Univ San Antonio Murcia UCAM Comp Engn Dept Bioinformat & High Performance Comp Res Grp BIOHP Murcia 30107 Spain Univ Murcia Int Doctorate Sch E-30100 Murcia Spain

Virtual screening (VS) methods have been shown to increase success rates in many drug discovery campaigns, when they complement experimental approaches, such as high-throughput screening methods or classical medicinal chemistry approaches. Nevertheless, predictive capability of VS is not yet optimal, mainly due to limitations in the underlying physical principles describing drug binding phenomena. One approach that can improve VS methods is the aid of machine learning methods. When enough experimental data are available to train such methods, predictive capability can considerably increase. We show in this research work how a multi-objective evolutionary search strategy for feature selection, which can provide with small and accurate decision trees that can be very easily understood by chemists, can drastically increase the applicability and predictive ability of these techniques and therefore aid considerable in the drug discovery problem. With the proposed methodology, we find classification models with accuracy between 0.9934 and 1.00 and area under ROC between 0.96 and 1.00 evaluated in full training sets, and accuracy between 0.9849 and 0.9940 and area under ROC between 0.89 and 0.93 evaluated with tenfold cross-validation over 30 iterations, while substantially reducing the model size.

关键词： Feature selection multi-objective evolutionary algorithms Classification Decision trees Virtual screening Drug discovery

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multi-objective evolutionary optimization using the relationship between F1 and accuracy metrics in classification tasks

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APPLIED INTELLIGENCE 2019年第9期49卷 3447-3463页

作者： Carlos Fernandez, Juan Carbonero, Mariano Antonio Gutierrez, Pedro Hervas-Martinez, Cesar Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain Univ Loyola Andalucia Dept Quantitat Methods Cordoba Spain

This work analyses the complementarity and contrast between two metrics commonly used for evaluating the quality of a binary classifier: the correct classification rate or accuracy, C, and the F-1 metric, which is very popular when dealing with imbalanced datasets. Based on this analysis, a set of constraints relating C and F-1 are defined as a function of the ratio of positive patterns in the dataset. We evaluate the possibility of using a multi-objective evolutionary algorithm guided by this pair of metrics to optimise binary classification models. To check the validity of the constraints, we perform an empirical analysis considering 26 benchmark datasets obtained from the UCI repository and an interesting liver transplant dataset. The results show that the relation is fulfilled and that the use of the algorithm for simultaneously optimising the pair (C,F-1) leads to a generally balanced accuracy for both classes. The experiments also reveal that, in some cases, better results are obtained by using the majority class as the positive class instead of using the minority one, which is the most common approach with imbalanced datasets.

关键词： Binary classification Evaluation metrics F-1-metric multi-objective evolutionary algorithms

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New hybrid between NSGA-III with multi-objective particle swarm optimization to multi-objective robust optimization design for Powertrain mount system of electric vehicles

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ADVANCES IN MECHANICAL ENGINEERING 2020年第2期12卷

作者： Nguyen Huy Truong Dinh-Nam Dao Inst Mil Mech Engn Hanoi Vietnam Le Quy Don Tech Univ Control Technol Coll Hanoi 100000 Vietnam

In this study, a new methodology, hybrid NSGA-III with multi-objective particle swarm optimization (HNSGA-III&MOPSO), has been developed to design and achieve cost optimization of Powertrain mount system stiffness parameters. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration and mean square displacement of the Powertrain mount system. A hybrid HNSGA-III&MOPSO is proposed with the integration of multi-objective particle swarm optimization and a genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&MOPSO is more efficient than the typical multi-objective particle swarm optimization, NSGA-III. Powertrain mount system stiffness parameter optimization with HNSGA-III&MOPSO is simulated, respectively. It proved the potential of the HNSGA-III&MOPSO for Powertrain mount system stiffness parameter optimization problem. The amplitude of the acceleration of the vehicle frame decreased by 22.8%, and the amplitude of the displacement of the vehicle frame reduced by 12.4% compared to the normal design case. The calculation time of the algorithm HNSGA-III&MOPSO is less than the algorithm NSGA-III, that is, 5 and 6 h, respectively, compared to the algorithm multi-objective particle swarm optimization.

关键词： NSGA-III algorithm multi-objective particle swarm optimization algorithm optimal solution Powertrain mount system stiffness multi-objective evolutionary algorithms mounting system

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OPLA-Tool v2.0: a Tool for Product Line Architecture Design Optimization 20

OPLA-Tool v2.0: a Tool for Product Line Architecture Design ...

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34th Brazilian Symposium on Software Engineering (SBES)

作者： Freire, Willian Marques Massago, Mamoru Zavadski, Arthur Cattaneo Malachini Miotto Amaral, Aline Maria Colanzi, Thelma Elita Univ Estadual Maringa Maringa Parana Brazil

ISBN: (纸本)9781450387538

The multi-objective Optimization Approach for Product Line Architecture Design (MOA4PLA) is the seminal approach that successfully optimizes Product Line Architecture (PLA) design using search algorithms. The tool named OPLA-Tool was developed in order to automate the use of MOA4PLA. Over time, the customization of the tool to suit the needs of new research and application scenarios led to several problems. The main problems identified in the original version of OPLA-Tool are environment configuration, maintainability and usability problems, and PLA design modeling and visualization. Such problems motivated the development of a new version of this tool: OPLA-Tool v2.0, presented in this work. In this version, those problems were solved by the source code refactoring, migration to a web-based graphical user interface (GUI) and inclusion of a new support tool for PLA modeling and visualization. Furthermore, OPLA-Tool v2.0 has new functionalities, such as new objective functions, new search operators, intelligent interaction with users during the optimization process, multi-user authentication and simultaneous execution of several experiments to PLA optimization. Such a new version of OPLA-Tool is an important achievement to PLA design optimization as it provides an easier and more complete way to automate this task.

关键词： Software product line product line architecture multi-objective evolutionary algorithms

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A Novel Outlook on Feature Selection as a multi-objective Problem 1

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14th Biennial International Conference on Artificial Evolution (EA)

作者： Barbiero, Pietro Lutton, Evelyne Squillero, Giovanni Tonda, Alberto Politecn Torino Turin Italy Univ Paris Saclay AgroParisTech INRA UMR 782 Thiverval Grignon France

ISBN: (数字)9783030457150

ISBN: (纸本)9783030457150

Feature selection is the process of choosing, or removing, features to obtain the most informative feature subset of minimal size. Such subsets are used to improve performance of machine learning algorithms and enable human understanding of the results. Approaches to feature selection in literature exploit several optimization algorithms. multi-objective methods also have been proposed, minimizing at the same time the number of features and the error. While most approaches assess error resorting to the average of a stochastic K-fold cross-validation, comparing averages might be misleading. In this paper, we show how feature subsets with different average error might in fact be non-separable when compared using a statistical test. Following this idea, clusters of nonseparable optimal feature subsets are identified. The performance in feature selection can thus be evaluated by verifying how many of these optimal feature subsets an algorithm is able to identify. We thus propose a multi-objective optimization approach to feature selection, EvoFS, with the objectives to i. minimize feature subset size, ii. minimize test error on a 10-fold cross-validation using a specific classifier, iii. maximize the analysis of variance value of the lowest-performing feature in the set. Experiments on classification datasets whose feature subsets can be exhaustively evaluated show that our approach is able to always find the best feature subsets. Further experiments on a high-dimensional classification dataset, that cannot be exhaustively analyzed, show that our approach is able to find more optimal feature subsets than state-of-the-art feature selection algorithms.

关键词： Feature selection Machine learning multi-objective optimization evolutionary algorithms multi-objective evolutionary algorithms

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