In many practical applications, data enrichment can generate a large amount of accurate data to alleviate the problem of data scarcity. In order to make the fake data generated in data enrichment as close to the real ...
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ISBN:
(纸本)9781728183923
In many practical applications, data enrichment can generate a large amount of accurate data to alleviate the problem of data scarcity. In order to make the fake data generated in data enrichment as close to the real data as possible, the data enriching model must be tuned to meet the loss requirements of multiple objectives in different scenarios, which makes it a multi-scenario many-objective optimization problem. However, due to the curse of the dimensionality of the scenario space and the objective space, the existing many-objectiveevolutionaryalgorithms cannot solve the problem in data enrichment well. To effectively handle this problem, we propose an adaptive formulation-based multi-objective evolutionary algorithm, where the aggregation function is used to reduce the dimension of the scenario space to one and the multiple objectives into three objectives through the adaptive formulation of the original problem. In this way, a multi-scenario many-objective problem is converted into a multi-objective problem which could be solved by existing multi-objective evolutionary algorithms. The proposed algorithm is applied to the practical data enrichment problem to solve the multi-scenario many-objective optimization problem and compared with NSGA-III. The experimental results demonstrate the remarkable superiority of the proposed algorithm over NSGA-III.
This paper proposed a surrogate-assisted dominance-based multi-objective evolutionary algorithm to solve multi-objective computationally expensive problems with medium dimensions. Two infill criteria are collaborative...
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This paper proposed a surrogate-assisted dominance-based multi-objective evolutionary algorithm to solve multi-objective computationally expensive problems with medium dimensions. Two infill criteria are collaboratively used to select promising individuals for exact evaluations. The convergence-based criterion is used to promote the exploitation of current promising areas. This criterion also considers the dispersion of selected solutions to exploit current non-dominant front. The diversity-based criterion is used to enhance the exploration of the population and enhance the accuracy of surrogate models. The feedback information from the convergence-based criterion is used to adjust the frequency of using the diversity-based criterion in order to reduce the consumed function evaluations. Benchmark functions with dimensions varying from 8 to 30 and a reactive power optimization problem are used to test the proposed algorithm. The experimental results demonstrate that the proposed algorithm significantly outperforms some state-of-the-art evolutionaryalgorithms on most problems.
As humanoid robots are expected to operate in human environments they are expected to perform a wide range of tasks. Therefore, the robot arm motion must be generated based on the specific task. In this paper we propo...
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As humanoid robots are expected to operate in human environments they are expected to perform a wide range of tasks. Therefore, the robot arm motion must be generated based on the specific task. In this paper we propose an optimal arm motion generation satisfying multiple criteria. In our method, we evolved neural controllers that generate the humanoid robot arm motion satisfying three different criteria;minimum time, minimum distance and minimum acceleration. The robot hand is required to move from the initial to the final goal position. In order to compare the performance, single objective GA is also considered as an optimization tool. Selected neural controllers from the Pareto solution are implemented and their performance is evaluated. Experimental investigation shows that the evolved neural controllers performed well in the real hardware of the mobile humanoid robot platform.
In this paper, a Two-Round learning-based algorithm for Continuous box-constrained multi-objectiveevolutionary optimization (TRACE) under the decomposition framework is proposed, in which the isomorphism relationship...
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In this paper, a Two-Round learning-based algorithm for Continuous box-constrained multi-objectiveevolutionary optimization (TRACE) under the decomposition framework is proposed, in which the isomorphism relationship between the clustered Pareto Front and Pareto solution set is explored and anew time-varying adaptive crossover operator is developed. The learning process involves two stages. In the first stage, the K-means is applied to cluster the population of objective vectors. By exploring the property of cluster-dependent isomorphism between the objective space and the decision space, a parent individual for each individual is selected from the corresponding clusters in the decision space. The time-varying adaptive crossover operator is then used together with the classical polynomial mutation operator to generate anew solution based on the selected parent individuals. As part of the environmental selection process, the K-means is applied again to the combination of parent and offspring individuals in the objective space to assist in the selection of suitable solutions for each decomposed subspace. TRACE is compared with 11 state-of-the-art multi-objective evolutionary algorithms on totally 43 difficult problems with different characteristics. Furthermore, TRACE is compared with three promising multi-objective evolutionary algorithms for community detection in attribute networks. Extensive experiments show that TRACE significantly outperforms the compared algorithms inmost instances.
作者:
Cheng, FanShu, ShengdaZhang, LeiTan, MingQiu, JianfengAnhui Univ
Inst Informat Mat Sch Artificial Intelligence Hefei 230601 Peoples R China Anhui Univ
Sch Artificial Intelligence Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ
Sch Comp Sci & Technol Hefei 230601 Peoples R China Hefei Univ
Sch Artificial Intelligence & Big Data Hefei 230601 Peoples R China
Maximizing receiver operating characteristic convex hull (ROCCH) is a hot research topic of binary classification, since it can obtain good classifiers under either balanced or imbalanced situation. Recently, evolutio...
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Maximizing receiver operating characteristic convex hull (ROCCH) is a hot research topic of binary classification, since it can obtain good classifiers under either balanced or imbalanced situation. Recently, evolutionaryalgorithms (EAs) especially multi-objective evolutionary algorithms (MOEAs) have shown their competitiveness in addressing the problem of ROCCH maximization. Thus, a series of MOEAs with promising performance have been proposed to tackle it. However, designing a MOEA for high-dimensional ROOCH maximization is much more challenging due to the "curse of dimension". To this end, in this paper, an evolutionarymultitasking approach (termed as EMT-ROCCH) is proposed, where a low-dimensional ROCCH maximization task T-a is constructed to assist the original high-dimensional task T-o. Specifically, in EMT-ROCCH, a low-dimensional assisted task T-a is firstly created. Then, two populations, P-a and P-o, are used to evolve tasks T-a and T-o, respectively. During the evolution, a knowledge transfer from P-a to P-o is designed to transfer the useful knowledge from P-a to accelerate the convergence of P-o. Moreover, a knowledge transfer from P-o to P-a is developed to utilize the useful knowledge in P-o to repair the individuals in P-a, aiming to avoid P-a being trapped into the local optima. Experiment results on 12 high-dimensional datasets have shown that compared with the state-of-the-arts, the proposed EMT-ROCCH could achieve ROCCH with higher quality.
Mine trucks, as the core equipment of discontinuous open-pit mining technology, account for high transportation costs and vast quantities of greenhouse gases. In order to improve transportation efficiency and decrease...
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Mine trucks, as the core equipment of discontinuous open-pit mining technology, account for high transportation costs and vast quantities of greenhouse gases. In order to improve transportation efficiency and decrease carbon emissions, rationally scheduling shovel-truck pairs is a necessary issue. Previous studies give less consideration on carbon emissions of trucks that varies with road and driving conditions. To overcome the shortage, a constraint bi-objective optimization model is built for low-carbon scheduling problem of open-pit mine trucks, in which minimizing both idle time and carbon emissions of trucks are taken as the objectives. More especially, the limits on working time, traffic volume and the number of trucks are modeled as the constraints. Carbon emissions is formulated by multistage nonlinear function that takes road condition, load and driving state of trucks into account. As the problem-solver, Q-learning assisted multi-objective evolutionary algorithm is put forward. Four evolution states are defined by analyzing the improvement on feasibility and convergence of the population, and four problem-specific evolution operators are designed to meet different demands of the evolution. Q-learning-based selection strategy is proposed to select the most appropriate operator, with the purpose of improving the evolution efficiency. Experimental results on the real-world instances expose that the proposed algorithm outperforms the other state-of-the-art algorithms significantly.
Feature selection (FS) is an important dimension reduction technique in practical applications, which has been widely studied during the past decades. Although a large number of FS algorithms have been proposed and sh...
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Feature selection (FS) is an important dimension reduction technique in practical applications, which has been widely studied during the past decades. Although a large number of FS algorithms have been proposed and shown the promising performance, most of them face with the challenge of "curse of dimensionality". To this end, inspired by evolutionarymultitasking (EMT), in this paper, a VariAble multiTasking-based multi-objective evolutionary algorithm, named VAMT-MOEA, is proposed for high-dimensional FS. For the existing EMT-based FS algorithms, they adopt the single or fixed assisted tasks to solve the high-dimensional FS problem (namely the original task). Once they trap into the local optima, it is difficult for them to provide valuable knowledge. Different from them, the proposed algorithm employs the variable multitasking scheme to achieve the feature subsets with high quality. The assisted task is adjusted with the changes of the weights in the evolution, where the weight measures the importance of each feature. Specifically, a variable-weight adjustment strategy is proposed to adjust the assisted task, aiming to overcome the loss of diversity during the evolution. Additionally, a novel knowledge transfer strategy is suggested, where the best and the worst solutions are used to implement the positive knowledge transfer between the assisted task and the original task. Finally, an initialization strategy is designed to generate an initial assisted task with competitiveness. The experimental results on 10 high-dimensional datasets, whose dimension ranges from 2,000 to 19,993, demonstrate the superiority of the proposed VAMT-MOEA in terms of the classification error, the number of selected features and the running time. To be specific, compared with five EA-based FS algorithms, the proposed VAMT-MOEA can achieve the feature subsets with lower classification error and smaller number of features. Moreover, the running time of different algorithms also reveal that
To enable mobile robots to safely and effectively accomplish path planning tasks on uneven terrains, this letter proposes a Gaussian adaptive strategy-based multi-objectiveevolutionary optimization. Firstly, path sol...
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To enable mobile robots to safely and effectively accomplish path planning tasks on uneven terrains, this letter proposes a Gaussian adaptive strategy-based multi-objectiveevolutionary optimization. Firstly, path solutions are generated by employing spline interpolation on the digital elevation model of the terrain map. Subsequently, a multi-objective optimization function is constructed, considering parameters such as path length, uniformity, slope, and relief. These parameters are computed using the elevation values of the terrain. Secondly, an evolutionary optimization algorithm based on a Gaussian adaptive strategy is introduced. This strategy ensures the uniform distribution, adaptability, and size stability of the reference points set. Additionally, it controls the selection rate of non-dominant contribution points during the optimization process. Finally, experimental results conducted in five different real environments demonstrate the suitability of the proposed algorithm for solving path planning problems on uneven terrain.
High-strength die steel is widely used for cutting, stamping and molding because of its advantages of high hardness, high toughness and abrasion resistance. However, its efficiency in cutting has always been difficult...
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High-strength die steel is widely used for cutting, stamping and molding because of its advantages of high hardness, high toughness and abrasion resistance. However, its efficiency in cutting has always been difficult. In a complex machining process, the optimization of cutting parameters has a significant influence on the stability and quality of products. In this study, an orthogonal experiment of NAK80 for milling is carried out with cutting speed, feed rate and depth of cut, all of which are considered as relevant variables. According to the experimental results, statistical models for cutting force and surface roughness are built using a regression method as objective functions. Superadded the theoretical formula of material removal rate, a multi-objective evolutionary algorithm based on decomposition is used to optimize the cutting parameters and a Pareto solution set is obtained finally.
Extraction of biologically-meaningful knowledge is one of the important and challenging tasks in bioinformatics, in particular computational analysis of DNA and protein sequences, in order to identify biological funct...
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Extraction of biologically-meaningful knowledge is one of the important and challenging tasks in bioinformatics, in particular computational analysis of DNA and protein sequences, in order to identify biological function(s) and behaviour(s) of newly-extracted sequences. Computational intelligence techniques in corporation with sequence-driven features have been applied to tackle the problem and help classify different functional classes of the sequences. In order to study this problem, subgroup discovery algorithms together with a signal processing-based feature extraction method are applied, where the sequences are represented as a signal. The applicability of this method has been studied through four different Neuraminidase genes of Influenza A subtypes, H1N1, H2N2, H3N2 and H5N1. The results yielded not only higher predictive accuracy over these four classes of the proteins but also interpretable rule-based representation of the descriptive model with a significantly reduced feature set driven by means of the signal processing method. Subgroup discovery technique based on evolutionary fuzzy systems is expected to open new areas of research in bioinformatics and further help identify and understand more focused therapeutic protein targets. (C) 2013 Elsevier B. V. All rights reserved.
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