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Fan Cheng

Bio: Fan Cheng is an academic researcher from Anhui University. The author has contributed to research in topics: Evolutionary algorithm & Computer science. The author has an hindex of 9, co-authored 30 publications receiving 442 citations. Previous affiliations of Fan Cheng include University of Science and Technology of China.

Papers
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Journal ArticleDOI
TL;DR: The proposed MOEA based on an enhanced inverted generational distance indicator is versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multiobjective and many-objective optimization.
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 multiobjective and many-objective optimization.

418 citations

Journal ArticleDOI
TL;DR: This work proposes an effective multi-objective pattern mining evolutionary algorithm for finding optimal pattern set, which does not need to specify the prior parameters min_sup, min_occ and m.
Abstract: Task-oriented pattern mining is to find the most popular and complete pattern for task-oriented applications such as goods match recommendation and print area recommendation. In these applications, the measure support is used to capture the popularity of patterns, while the measure occupancy is adopted to capture the completeness of patterns. Existing methods for mining task-oriented patterns usually combine these two measures as one measure for optimization, and require users to set the prior parameters such as the minimum support threshold min_sup, the minimum occupancy threshold min_occ and the relative importance preference l between support and occupancy. However, it is very difficult for users to set optimal values for these parameters especially when they do not have any prior knowledge in real applications. To overcome this challenge, we propose an evolutionary approach for pattern mining from a multi-objective perspective since support and occupancy are conflicting. Specifically, we first transform this pattern mining problem into a multi-objective optimization problem. Then we propose an effective multi-objective pattern mining evolutionary algorithm for finding optimal pattern set, which does not need to specify the prior parameters min_sup, min_occ and m. Finally, we select k best patterns from the obtained pattern set for final pattern recommendation. Experimental results on two real task-oriented applications, namely, goods match recommendation in Taobao and print area recommendation in SmartPrint, and several large synthetic datasets demonstrate the promising performance of the proposed method in terms of both effectiveness and efficiency.

55 citations

Journal ArticleDOI
Lei Zhang1, Guanglong Fu1, Fan Cheng1, Jianfeng Qiu1, Yansen Su1 
TL;DR: A multi-objective itemset mining algorithm is proposed for solving the transformed problem, which can provide multiple itemsets recommendation for decision-makers in only one run and does not need to specify the prior parameters, which brings much convenience to users.

49 citations

Journal ArticleDOI
TL;DR: A local information based MOEA, termed LMOEA, is proposed for community detection, where an individual updating strategy is suggested to improve the quality of community detection.

34 citations

Journal ArticleDOI
TL;DR: In the proposed CSE, the community structure of a network is enhanced by adding links between the nodes possibly belonging to the same community and reducing links between those belonging to different communities, thereby converting an ambiguous community structure into a structure much clearer than the original one.
Abstract: Community detection has been recognized as one of the most important tools to discover useful information hidden in complex networks which is usually hard to be obtained by simple observations. Existing community detection algorithms have demonstrated their effectiveness on a variety of complex networks, most of them, however, suffer from the scalability issue on complex networks without a clear community structure due to the challenge in the detection of ambiguous community structure. To address this issue, in this paper, we propose a community structure enhancement method, termed CSE, for community detection in complex networks. In the proposed CSE, the community structure of a network is enhanced by adding links between the nodes possibly belonging to the same community and reducing links between those belonging to different communities, thereby converting an ambiguous community structure into a structure much clearer than the original one. The experimental results show the superior performance of the proposed CSE over five state-of-the-art community detection algorithms on both synthetic benchmark networks and real-world networks, especially for those without a clear community structure.

34 citations


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Journal ArticleDOI
TL;DR: This handbook is a very useful handbook for engineers, especially those working in signal processing, and provides real data bootstrap applications to illustrate the theory covered in the earlier chapters.
Abstract: tions. Bootstrap has found many applications in engineering field, including artificial neural networks, biomedical engineering, environmental engineering, image processing, and radar and sonar signal processing. Basic concepts of the bootstrap are summarized in each section as a step-by-step algorithm for ease of implementation. Most of the applications are taken from the signal processing literature. The principles of the bootstrap are introduced in Chapter 2. Both the nonparametric and parametric bootstrap procedures are explained. Babu and Singh (1984) have demonstrated that in general, these two procedures behave similarly for pivotal (Studentized) statistics. The fact that the bootstrap is not the solution for all of the problems has been known to statistics community for a long time; however, this fact is rarely touched on in the manuscripts meant for practitioners. It was first observed by Babu (1984) that the bootstrap does not work in the infinite variance case. Bootstrap Techniques for Signal Processing explains the limitations of bootstrap method with an example. I especially liked the presentation style. The basic results are stated without proofs; however, the application of each result is presented as a simple step-by-step process, easy for nonstatisticians to follow. The bootstrap procedures, such as moving block bootstrap for dependent data, along with applications to autoregressive models and for estimation of power spectral density, are also presented in Chapter 2. Signal detection in the presence of noise is generally formulated as a testing of hypothesis problem. Chapter 3 introduces principles of bootstrap hypothesis testing. The topics are introduced with interesting real life examples. Flow charts, typical in engineering literature, are used to aid explanations of the bootstrap hypothesis testing procedures. The bootstrap leads to second-order correction due to pivoting; this improvement in the results due to pivoting is also explained. In the second part of Chapter 3, signal processing is treated as a regression problem. The performance of the bootstrap for matched filters as well as constant false-alarm rate matched filters is also illustrated. Chapters 2 and 3 focus on estimation problems. Chapter 4 introduces bootstrap methods used in model selection. Due to the inherent structure of the subject matter, this chapter may be difficult for nonstatisticians to follow. Chapter 5 is the most impressive chapter in the book, especially from the standpoint of statisticians. It provides real data bootstrap applications to illustrate the theory covered in the earlier chapters. These include applications to optimal sensor placement for knock detection and land-mine detection. The authors also provide a MATLAB toolbox comprising frequently used routines. Overall, this is a very useful handbook for engineers, especially those working in signal processing.

1,292 citations

01 Jan 2010
TL;DR: The work is giving estimations of the discrepancy between solutions of the initial and the homogenized problems for a one{dimensional second order elliptic operators with random coeecients satisfying strong or uniform mixing conditions by introducing graphs representing the domain of integration of the integrals in each term.
Abstract: The work is giving estimations of the discrepancy between solutions of the initial and the homogenized problems for a one{dimensional second order elliptic operators with random coeecients satisfying strong or uniform mixing conditions. We obtain several sharp estimates in terms of the corresponding mixing coeecient. Abstract. In the theory of homogenisation it is of particular interest to determine the classes of problems which are stable on taking the homogenisation limits. A notable situation where the limit enlarges the class of original problems is known as memory (nonlocal) eeects. A number of results in that direction has been obtained for linear problems. Tartar (1990) innitiated the study of the eeective equation corresponding to nonlinear equation: @ t u n + a n u 2 n = f: Signiicant progress has been hampered by the complexity of required computations needed in order to obtain the terms in power{series expansion. We propose a method which overcomes that diiculty by introducing graphs representing the domain of integration of the integrals in each term. The graphs are relatively simple, it is easy to calculate with them and they give us a clear image of the form of each term. The method allows us to discuss the form of the eeective equation and the convergence of power{series expansions. The feasibility of our method for other types of nonlinearities will be discussed as well.

550 citations

Journal ArticleDOI
TL;DR: This article summarises and categorises 100 state-of-the-art quality indicators and discusses issues regarding attributes that indicators possess and properties that indicators are desirable to have, in the hope of motivating researchers and practitioners to look into these important issues when designing quality indicators.
Abstract: Complexity and variety of modern multiobjective optimisation problems result in the emergence of numerous search techniques, from traditional mathematical programming to various randomised heuristics. A key issue raised consequently is how to evaluate and compare solution sets generated by these multiobjective search techniques. In this article, we provide a comprehensive review of solution set quality evaluation. Starting with an introduction of basic principles and concepts of set quality evaluation, this article summarises and categorises 100 state-of-the-art quality indicators, with the focus on what quality aspects these indicators reflect. This is accompanied in each category by detailed descriptions of several representative indicators and in-depth analyses of their strengths and weaknesses. Furthermore, issues regarding attributes that indicators possess and properties that indicators are desirable to have are discussed, in the hope of motivating researchers to look into these important issues when designing quality indicators and of encouraging practitioners to bear these issues in mind when selecting/using quality indicators. Finally, future trends and potential research directions in the area are suggested, together with some guidelines on these directions.

228 citations

Journal ArticleDOI
TL;DR: This paper proposes a competitive mechanism based multi-objective particle swarm optimizer, where the particles are updated on the basis of the pairwise competitions performed in the current swarm at each generation.

219 citations

Journal ArticleDOI
TL;DR: A novel dominance relation is proposed to better balance convergence and diversity for evolutionary many-objective optimization, where only the best converged candidate solution is identified to be nondominated in each niche.
Abstract: Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most existing dominance relations show poor performance in balancing them, thus easily leading to a set of solutions concentrating on a small region of the Pareto fronts. In this paper, a novel dominance relation is proposed to better balance convergence and diversity for evolutionary many-objective optimization. In the proposed dominance relation, an adaptive niching technique is developed based on the angles between the candidate solutions, where only the best converged candidate solution is identified to be nondominated in each niche. Experimental results demonstrate that the proposed dominance relation outperforms existing dominance relations in balancing convergence and diversity. A modified NSGA-II is suggested based on the proposed dominance relation, which shows competitiveness against the state-of-the-art algorithms in solving many-objective optimization problems. The effectiveness of the proposed dominance relation is also verified on several other existing multi- and many-objective evolutionary algorithms.

200 citations