Dunwei Gong

China University of Mining Technology, Suchow, Jiangsu Sheng, China

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Publications (47)8.67 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Interval multi-objective optimization problems (IMOPs), whose parameters are intervals, are considerably ubiquitous in real-world applications. Previous evolutionary algorithms (EAs) aim at finding the well-converged and evenly-distributed Pareto front. An EA incorporating with a decision-maker (DM)׳s preferences was presented in this study to obtain a Pareto-optimal subset that meets the DM׳s preferences. In this algorithm, the DM׳s preferences in terms of the relative importance of objectives were interactively input, and the corresponding preferred regions were then obtained. Based on these regions, solutions with the same rank were further distinguished to guide the search towards the DM׳s preferred region. The proposed method was empirically evaluated on four IMOPs and compared with other state-of-the-art methods. The experimental results demonstrated the simplicity and the effectiveness of the proposed method.
    Neurocomputing 01/2014; 137:241–251. · 1.63 Impact Factor
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    ABSTRACT: Interactive genetic algorithm (IGA), combining a user's intelligent evaluation with the traditional operators of genetic algorithms, are developed to optimize those problems with aesthetic indicators. The evaluation uncertainties and burden, however, greatly restrict the applications of IGA in complicated situations. Surrogate model approximating to the evaluation of the user has been generally applied to alleviate the evaluation burden of the user. The evaluation uncertainties, however, are not taken into account in existing research, therefore, a weighted multi-output gaussian process is here proposed to build the surrogate model by incorporating the uncertainty so as to enhance the performance of IGA. First, an IGA with interval fitness evaluation is adopted to depict the evaluation uncertainty, and the evaluation noise is defined based on the assignment. With the evaluation noise, the weight of each training sample is calculated and used to train a gaussian process which has two outputs to approximate the upper and lower values of the interval fitness, respectively. The trained gaussian process is treated as a fitness function and used to estimate the fitness of individuals generated in the subsequent evolutions. The proposed algorithm is applied to a benchmark function and a real-world fashion design to experimentally demonstrate its strength in searching.
    Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), 2013 IEEE Symposium on; 01/2013
  • Dunwei Gong, Gengxing Wang, Xiaoyan Sun
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    ABSTRACT: Many-objective optimization problems are very common and important in real-world applications, and there exist few methods suitable for them. Therefore, many-objective optimization problems are focused on in this study, and a set-based genetic algorithm is presented to effectively solve them. First, each objective of the original optimization problem is transformed into a desirability function according to the preferred region defined by the decision-maker. Thereafter, the transformed problem is further converted to a bi-objective optimization one by taking hyper-volume and the decision-maker's satisfaction as the new objectives, and a set of solutions of the original optimization problem as the new decision variable. To tackle the converted bi-objective optimization problem by using genetic algorithms, the crossover operator inside a set is designed based on the simplex method by using solutions of the original optimization problem, and the crossover operator between sets is developed by using the entropy of sets. In addition, the mutation operator of a set is presented to obey the Gaussian distribution and change along with the decision-maker's preferences. The proposed method is applied to five benchmark many-objective optimization problems, and compared with other six methods. The experimental results empirically demonstrate its effectiveness.
    Computational Intelligence (UKCI), 2013 13th UK Workshop on; 01/2013
  • Dunwei Gong, Xinfang Ji
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    ABSTRACT: Multi-objective optimization problems with interval parameters are ubiquitous and important, yet not many effective methods are available for solving them. To solve these problems, we propose a set-based evolutionary algorithm incorporated with decision-maker (DM)’s preferences to obtain a Pareto solution set which satisfies DM’s preferences. In this algorithm, the original optimization problem is first transformed into a tri-objective deterministic optimization problem with three performance indicators: hyper-volume, uncertainty and DM satisfaction. To solve the transformed problem, we employ a set-based Pareto dominance relation to compare different individuals. Individuals with the same rank are distinguished by using a specially designed extension measure incorporating DM’s preferences. Additionally, a set-based mutation and recombination scheme is suggested to generate an offspring with high performance. Four benchmark multiobjective optimization problems and a car cab design problem are used to evaluate the proposed method. Results are compared with those from other three methods. Conclusions indicate that the proposed method can obtain a Pareto solution set with a desirable compromise among the convergence, extension, uncertainty and the DM’s satisfaction.
    Kongzhi Lilun Yu Yinyong/Control Theory and Applications 01/2013; 30(11).
  • Jing Sun, Dunwei Gong, Xinfang Ji
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    ABSTRACT: Interval multi-objective optimization problems are ubiquitous and important in real-world applications. An interactive evolutionary algorithm incorporating an optimization-cum-decision-making procedure is presented to obtain the most preferred solution that fits a decision-maker (DM)’s preferences. In this algorithm, a preference direction is elicited by requesting the DM to select the worst one from a part of non-dominated solutions. A metric based on the above direction, which reflects the approximation performance of a candidate solution, is designed to rank different solutions with the same rank and preference. The proposed method is applied to four interval bi-objective optimization problems, and compared with PPIMOEA as well as an a posteriori method. The experimental results show the effectiveness and high efficiency of the proposed method.
    Kongzhi yu Juece/Control and Decision 01/2013; 28(4).
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    ABSTRACT: An interactive genetic algorithm with evaluating individuals using variational granularity was presented in this study to effectively alleviate user fatigue. In this algorithm, multiple language sets with different evaluation granularities are provided. The diversity of a population described with the entropy of its gene meaning units is utilized to first choose parts of appropriate language sets to participate in evaluating the population. A specific language set for evaluating an individual is further selected from these sets according to the distance between the individual and the current preferred one. The proposed algorithm was applied to a curtain evolutionary design system and compared with previous typical ones. The empirical results demonstrate the strengths of the proposed algorithm in both alleviating user fatigue and improving the efficiency in search.
    Proceedings of the 19th international conference on Neural Information Processing - Volume Part III; 11/2012
  • Dunwei Gong, Tian Tian, Xiangjuan Yao
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    ABSTRACT: Generating test data covering multiple paths using multi-population parallel genetic algorithms is a considerable important method. The premise on which the method above is efficient is appropriately grouping target paths. Effective methods of grouping target paths, however, have been absent up to date. The problem of grouping target paths for generation of test data covering multiple paths is investigated, and a novel method of grouping target paths is presented. In this method, target paths are divided into several groups according to calculation resources available and similarities among target paths, making a small difference in the number of target paths belonging to different groups, and a great similarity among target paths in the same group. After grouping these target paths, a mathematical model is built for parallel generation of test data covering multiple paths, and a multi-population genetic algorithm is adopted to solve the model above. The proposed method is applied to several benchmark or industrial programs, and compared with a previous method. The experimental results show that the proposed method can make full use of calculation resources on the premise of meeting the requirement of path coverage, improving the efficiency of generating test data.
    Journal of Systems and Software 11/2012; 85(11):2531–2540. · 1.14 Impact Factor
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    ABSTRACT: Surrogate-assisted interactive genetic algorithms (IGAs) are found to be very effective in reducing human fatigue. Different from models used in most surrogate-assisted evolutionary algorithms, surrogates in IGA must be able to handle the inherent uncertainties in fitness assignment by human users, where, e.g., interval-based fitness values are assigned to individuals. This poses another challenge to using surrogates for fitness approximation in evolutionary optimization, in addition to the lack of training data. In this paper, a new surrogate-assisted IGA has been proposed, where the uncertainty in subjective fitness evaluations is exploited both in training the surrogates and in managing surrogates. To enhance the approximation accuracy of the surrogates, an improved cotraining algorithm for semisupervised learning has been suggested, where the uncertainty in interval-based fitness values is taken into account in training and weighting the two cotrained models. Moreover, uncertainty in the interval-based fitness values is also considered in model management so that not only the best individuals but also the most uncertain individuals will be chosen to be re-evaluated by the human user. The effectiveness of the proposed algorithm is verified on two test problems as well as in fashion design, a typical application of IGA. Our results indicate that the new surrogate-assisted IGA can effectively alleviate user fatigue and is more likely to find acceptable solutions in solving complex design problems.
    IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society 09/2012; · 3.01 Impact Factor
  • Xiaoyan Sun, Dunwei Gong, Wei Zhang
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    ABSTRACT: Interactive genetic algorithms are effective methods of solving optimization problems with implicit (qualitative) criteria by incorporating a user's intelligent evaluation into traditional evolution mechanisms. The heavy evaluation burden of the user, however, is crucial and limits their applications in complex optimization problems. We focus on reducing the evaluation burden by presenting a semi-supervised learning assisted interactive genetic algorithm with large population. In this algorithm, a population with many individuals is adopted to efficiently explore the search space. A surrogate model built with an improved semi-supervised learning method is employed to evaluate a part of individuals instead of the user to alleviate his/her burden in evaluation. Incorporated with the principles of the improved semi-supervised learning, the opportunities of applying and updating the surrogate model are determined by its confidence degree in estimation, and the informative individuals reevaluated by the user are selected according to the concept of learning from mistakes. We quantitatively analyze the performance of the proposed algorithm and apply it to the design of sunglasses lenses, a representative optimization problem with one qualitative criterion. The empirical results demonstrate the strength of our algorithm in searching for satisfactory solutions and easing the evaluation burden of the user.
    Applied Soft Computing. 09/2012; 12(9):3004–3013.
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    ABSTRACT: Traditional methods of generating test data may result in redundancy of test data, which brings many troubles to software testing. In order to solve the redundancy of test data, this study proposed a novel approach of generating test data by reducing target statements based on dominant relations. First, basic concepts and principles concerning dominance are listed. Then, an approach is proposed to reduce target statements according to their dominant relations. Finally, test suite covering the reduced set of target statements is generated by a genetic algorithm. The generated test suite can also cover all original target statements, which is guaranteed by the proposed strategy. We applied the method to nine benchmark programs, and compared with traditional and greedy methods. The experimental results show that our method can not only reduce redundancy, but also improve the efficiency of generating test data.
    Evolutionary Computation (CEC), 2012 IEEE Congress on; 01/2012
  • Xiaoyan Sun, Lei Yang, Dunwei Gong, Ming Li
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    ABSTRACT: Interactive genetic algorithms (IGAs) have been successfully applied to optimize problems with aesthetic criteria by embedding the intelligent evaluations of a user into the evolutionary process. User fatigue caused by frequent interactions, however, often greatly impairs the potentials of IGAs on solving complicated optimization problems. Taking the benefits of collective intelligence into account, we here present an IGA with collective intelligence which is derived from a mechanism of group decision making. An IGA with interval individual fitness is focused here and it can be separately conducted by multiple users at the same time. The collective intelligence of all participated users, represented with social and individual knowledge, is first collected by using a modified group decision making method. Then the strategy of applying the collective intelligence to initialize and guide the single evolution of the IGA is given. With such a multi-user promoted IGA framework, the performance of a single IGA is expected to be evidently improved. In a local network environment, the algorithm is applied to a fashion design system and the results empirically demonstrate that the algorithm can not only alleviate user fatigue but also increase the opportunities of IGAs on finding most satisfactory solutions.
    Evolutionary Computation (CEC), 2012 IEEE Congress on; 01/2012
  • Dunwei Gong, Lei Yang, Xiaoyan Sun, Ming Li
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    ABSTRACT: Interactive genetic algorithms (IGAs) are effective methods of solving optimization problems with qualitative indices. The problem of user fatigue resulting from his/her evaluations, however, restricts their applications in complex optimization problems. Employing various surrogate models to evaluate (a part of) individuals instead of a user is a feasible approach to solving the above problem. Previous studies, however, have not fully utilized knowledge provided by users with similar preference when constructing these models. The problem of constructing surrogate models by using knowledge of users with similar preference was focused in this study. First, users with similar preference participating the evolution were identified based on the matrix formed by the relationship between users and the “fitness” of allele meaning units and the users' interests in allele meaning units by using the collaborative filtering algorithm based on nearest-neighbor; and then the individuals evaluated by users with similar preference and chosen according to the users' preference similarities and confidence, along with their fitness, were as a part of samples for training the surrogate model of the current user's cognition. The proposed method was applied to an evolutionary fashion design system, and the experimental results show that the proposed method can improve the capability in exploration on the premise of greatly alleviating user fatigue.
    Evolutionary Computation (CEC), 2012 IEEE Congress on; 01/2012
  • Lei Yang, Dunwei Gong, Xiaoyan Sun, Jing Sun
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    ABSTRACT: Interactive genetic algorithms (IGAs) are effective methods of tackling optimization problems involving qualitative indices by incorporating a user's evaluations into traditional genetic algorithms. The problem of user fatigue resulting from the user's evaluations, however, has a negative influence on the performance of these algorithms. Substituting the user's evaluations with various surrogate models is beneficial to alleviate user fatigue. Previous studies, however, have not taken full advantage of information provided by samples obtained earlier when constructing or updating these models. We focus on the issue of user fatigue in this study, and present a novel method of effectively alleviating user fatigue by substituting the user's evaluations with a weighted support vector machine (WSVM) and by incorporating it with the mechanism of transfer learning. The proposed method is applied to the fashion evolutionary design system and compared with previous effective IGAs. The experimental results confirm the advantage of the proposed method in both alleviating user fatigue and improving the precision of the surrogate model.
    Intelligent Control and Automation (WCICA), 2012 10th World Congress on; 01/2012
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    ABSTRACT: Problems with hybrid indices are common, but the performance of previous methods in solving these problems needs to be further improved. We propose an adaptive evolutionary optimization algorithm of solving the above problems effectively in this study. First, the convergence rate of a population is calculated based on the distance between the optimal individuals with adjacent generations; then the crossover and mutation probabilities are adjusted dynamically according to the diversity, the convergence rate of a population and the number of generations. We apply the proposed algorithm to an interior layout design problem, a typical optimization problem with hybrid indices, and compare it with the algorithm with constant crossover and mutation probabilities. The experimental results confirm that the proposed algorithm has advantages in the number, quality and distribution of optimal solutions. The proposed algorithm has a good tradeoff between exploration and exploitation, and provides an efficient way to solve an optimization problem with hybrid indices.
    Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on; 01/2011
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    Dunwei Gong, Wanqiu Zhang, Xiangjuan Yao
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    ABSTRACT: Highlights • The first study to consider the problem of generating test data for many target paths. • The first study to establish the optimization model for the problem of generating test data for many paths coverage. • Propose a strategy of grouping target paths in order to reduce the difficulty of the problem. • Propose a strategy of reconstructing sub-optimization problems during the evolution to reduce the difficulty further.
    Journal of Systems and Software 01/2011; 84:2222-2233. · 1.14 Impact Factor
  • Jing Sun, Dunwei Gong, Xiaoyan Sun
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    ABSTRACT: Interval multi-objective optimization problems (MOPs) are popular and important in real-world applications. We present a novel interactive evolutionary algorithm (IEA) incorporating an optimization-cum-decision-making procedure to obtain the most preferred solution that fits a decision-maker (DM)'s preferences. Our method is applied to two interval MOPs and compared with PPIMOEA and the posteriori method, and the experimental results confirm the superiorities of our method.
    Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II; 01/2011
  • Dunwei Gong, Jie Yuan
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    ABSTRACT: User fatigue problem in traditional interactive genetic algorithms restricts the population size. It is necessary to maintain large population size in order to apply these algorithms to optimize complicated problems. We present a large population size interactive genetic algorithm with an individual’s fitness not assigned by the user in this paper. The algorithm divides a population into several clusters, and the maximum number of clusters is changeable with the evolution and the distribution of the population. A user only evaluates one representative individual in each cluster, and others’ fitness are estimated based on these representative ones. In addition, to assign a representative individual’s fitness, we record time when the user evaluates it satisfactory or unsatisfactory according to his/her sensibility, and its fitness is automatically calculated based on the time. Finally, we apply the proposed algorithm in a fashion evolutionary design system, and compare it with other two IGAs each of which has one aspect, including the population size and the evaluation method, the same as the proposed algorithm. The experimental results validate its efficiency.
    Appl. Soft Comput. 01/2011; 11:936-945.
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    Jing Sun, Dunwei Gong, Xiaoyan Sun
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    ABSTRACT: Multi-objective optimization (MOO) problems with interval parameters are popular and important in real-world applications. Previous evolutionary optimization methods aim to find a set of well-converged and evenly-distributed Pareto-optimal solutions. We present a novel evolutionary algorithm (EA) that interacts with a decision maker (DM) during the optimization process to obtain the DM's most preferred solution. First, the theory of a preference polyhedron for an optimization problem with interval parameters is built up. Then, an interactive evolutionary algorithm (IEA) for MOO problems with interval parameters based on the above preference polyhedron is developed. The algorithm periodically provides a part of non-dominated solutions to the DM, and a preference polyhedron, based on which optimal solutions are ranked, is constructed with the worst solution chosen by the DM as the vertex. Finally, our method is tested on two bi-objective optimization problems with interval parameters using two different value function types to emulate the DM's responses. The experimental results show its simplicity and superiority to the posteriori method.
    13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Proceedings, Dublin, Ireland, July 12-16, 2011; 01/2011
  • Yan Zhang, Dunwei Gong, Yongjin Luo
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    ABSTRACT: The aim of software testing is to find faults in the program under test. Previous methods of path-oriented test data generation can generate test data traversing target paths, but they may not guarantee to find faults in the program. We present a method of evolutionary generation of test data for path coverage with faults detection in this paper. First, we establish a mathematical model of the problem considered in this paper, in which the number of faults detected in the path traversed by test data, and the risk level of faults are optimization objectives, and the approach level of the traversed path from the target one is a constraint. Then, we generate test data using a multi-objective evolutionary optimization algorithm with constraints. Finally, we apply the proposed method in a benchmark program bubble sort and an industrial program totinfo, and compare it with the traditional method. The experimental results conform that our method can generate test data that not only traverse the target path but also detect faults in it. Our achievement provides a novel way to generate test data for path coverage with faults detection.
    Seventh International Conference on Natural Computation, ICNC 2011, Shanghai, China, 26-28 July, 2011; 01/2011
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    Yan Zhang, Dunwei Gong
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    ABSTRACT: The aim of software testing is to find faults in the program under test. Generating test data which can reveal faults is the core issue. Although existing methods of path-oriented testing can generate test data which traverse target paths, they cannot guarantee that the data find the faults in the program. In this paper, we transform the problem into a multi-objective optimization problem with constrains and propose a method of evolutionary generation of test data for multiple paths coverage with faults detection. First, we establish the mathematical model of this problem and then a strategy based on multi-objective genetic algorithms is given. Finally we apply the proposed method in some programs under test and the experimental results validate that our method can find specified faults effectively. Compared with other methods of test data generation for multiple paths coverage, our method has greater advantage in faults detection and testing efficiency.
    Bio-Inspired Computing: Theories and Applications (BIC-TA), 2010 IEEE Fifth International Conference on; 10/2010