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| 1 | Adaptive interactive genetic algorithms with individual interval fitness显示文摘It is necessary to enhance the performance of interactive genetic algorithms in order to apply them to complicated optimization prob- lems successfully. An adaptive interactive genetic algorithm with individual interval fitness is proposed in this paper in which an indi- vidual fitness is expressed by an interval. Through analyzing the fitness, information reflecting the distribution of an evolutionary population is picked up, namely, the difference of evaluating superior individuals and the difference of evaluating a population. Based on these, the adaptive probabilities of crossover and mutation operators of an individual are presented. The algorithm proposed in this paper is applied to a fashion evolutionary design system, and the results show that it can find many satisfactory solutions per generation. The achievement of the paper provides a new approach to enhance the performance of interactive genetic algorithms. | Dunwei Gong Guangsong Guo Li Lu Hongmei Ma | 2008 | Progress in Natural Science:Materials International2008，18，3： | 13 |
| 2 | Evolutionary Generation Approach of Test Data for Multiple Paths Coverage of Message-passing Parallel Programs显示文摘Test data generation, the premise of software testing, has attracted scholars in the software engineering community in recent years. Influenced by task partitioning, process scheduling, and network delays, parallel programs are executed in a non-deterministic way, which makes test data generation of parallel programs different from that of serial programs in essence. This paper investigated the problem of generating test data for multiple paths coverage of message-passing parallel programs. A mathematical model of the above problem was built based on each given path and its equivalent ones. It was solved by using a genetic algorithm to generate all desired data in one run. The proposed method was applied to five benchmark programs, and compared with the existing methods.The experimental results show that the proposed method greatly shortens the number of iterations and time consumption without reducing the coverage rate. | TIAN Tian GONG Dunwei | 2014 | Chinese Journal of Electronics2014，23，2： | 10 |
| 3 | Evolutionary Generation of Test Data for Multiple Paths Coverage显示文摘 | GONG Dunwei ZHANG Wanqiu ZHANG Yan | 2011 | Chinese Journal of Electronics2011，20，2： | 8 |
| 4 | Surrogate Models Based on Individual's Interval Fitness in Interactive Genetic Algorithms显示文摘 | GONG Dunwei GONG Dunwei SUN Xiaoyan SUN Xiaoyan REN Jie REN Jie | 2009 | Chinese Journal of Electronics2009，18，4： | 5 |
| 5 | Solving Interval Multi-objective Optimization Problems Using Evolutionary Algorithms with Lower Limit of Possibility Degree显示文摘 | SUN Jing GONG Dunwei | 2013 | Chinese Journal of Electronics2013，22，2： | 5 |
| 6 | Interactive Genetic Algorithms with Individual's Fuzzy and Stochastic Fitness显示文摘 | SUN Xiaoyan SUN Xiaoyan GONG Dunwei GONG Dunwei | 2009 | Chinese Journal of Electronics2009，18，4： | 5 |
| 7 | Generating test data for both path coverage and fault detection using genetic algorithms显示文摘 | Dunwei GONG Yan ZHANG | 2013 | Frontiers of Computer Science2013，7，6： | 4 |
| 8 | Generating test data for both paths coverage and faults detection using genetic algorithms: multi-path case显示文摘产生能暴露程序的差错的测试数据是在软件测试的一个重要问题。尽管盖住路径的以前的方法能产生测试数据穿越目标路径,这些方法产生的测试数据在检测在盖住的路径上躺着的一些低概率的差错是困难的。我们在场为盖住多重路径检测产生测试数据的一个方法在这指责学习。首先,我们转变盖住多重路径并且与限制检测差错进一个多客观的优化问题的问题,并且为它构造一个数学模型。然后,我们给基于一个加权的基因算法解决模型的策略。最后,我们把我们的方法用于几个真实世界的程序,并且把它与几个方法作比较。试验性的结果证实建议方法罐头更高效地产生测试数据不仅穿越目标路径而且检测比另外的方法躺在他们的差错。 | Yan ZHANG Dunwei GONG | 2014 | Frontiers of Computer Science2014，8，5： | 4 |
| 9 | Test Data Generation for Multiple Paths Based on Local Evolution显示文摘Generating test data by genetic algorithms is a promising research direction in software testing, among which path coverage is an important test method. The efficiency of test data generation for multi-path coverage needs to be further improved. We propose a test data generation method for multi-path coverage based on a genetic algorithm with local evolution. The mathematical model is established for all target paths, while in the algorithm the individuals are evolved locally according to different objective functions. We can improve the utilization efficiency of test data. The computation cost can be reduced by using fitness functions of different granularity in different phases of the algorithm. | YAO Xiangjuan GONG Dunwei WANG Wenliang | 2015 | Chinese Journal of Electronics2015，24，1： | 4 |
| 10 | Applying Knowledge of Users with Similar Preference to Construct Surrogate Models of IGA显示文摘Interactive genetic algorithms(IGAs) are effective methods of solving optimization problems with qualitative indices. The problem of user fatigue resulting from the user's evaluations has a negative influence on the performance of these algorithms. Employing various surrogate models to evaluate(a part of) individuals instead of a user is a feasible approach to solve the problem. Previous studies have not fully utilized knowledge provided by users with a similar preference when constructing these models.The problem of constructing surrogate models by using the knowledge of users with a similar preference was focused in this study. Users with a similar preference participating in the evolution were identified by using the collaborative filtering algorithm based on the nearest neighbor, and the individuals evaluated by these users were chosen 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 showed that the proposed method can improve the capability in exploration on the premise of greatly alleviating user fatigue. | GONG Dunwei YANG Lei SUN Xiaoyan | 2015 | Chinese Journal of Electronics2015，24，3： | 2 |
| 11 | A discrete ar- tificial bee colony algorithm incorporating differential evolu- tion for the flowshop scheduling problem with blocking显示文摘 | HAN Yuyan GONG Dunwei SUN Xiaoyan | 2015 | Engineering Optimization2015，47，7： | 1 |
| 12 | Handing multiobjective optimization problem with a multi-swarm cooperative particle swarm optimizer显示文摘 | Zhang Yong Gong Dunwei Ding Honghai | 2011 | Expert System with Application2011，38，13： | 1 |
| 13 | Interactive genetic algorithms with large population and semi-supervised learning 显示文摘 | Sun Xiaoyan Gong Dunwei Zhang Wei | 2012 | Applied Soft Computing2012，12，： | 1 |
| 14 | Evolu- tionary generation of test data for many paths coverage based on grouping 显示文摘 | Gong Dunwei Zhang Wanqiu Yao Xiangjuan | 2011 | J Syst Software2011，84，12： | 1 |
| 15 | Evolutionary algorithms for optimization problems with uncertainties and hybrid indices显示文摘 | GONG Dunwei QIN Nana SUN Xiaoyan | 2011 | Information Sciences2011，181，19： | 1 |
| 16 | Large Population Size IGA with Individuals’ Fitness Not Assigned by User显示文摘 | Gong Dunwei Yuan Jie | 2011 | Applied Soft Computing2011，11，1： | 1 |
| 17 | Hybrid bare-bones PSO for dynamic economic dispatch with valve-point effects显示文摘 | Zhang Yong Gong Dunwei Geng Na | 2014 | Applied Soft Computing2014，18，5： | 1 |
| 18 | Interactive Genetic Algorithms with Interval Fitness of Evolutionary Individuals显示文摘 | Gong Dunwei Guo Guangsong | 2007 | Dynamics of Continuous Discrete and Impulsive Systems2007，14，2： | 1 |
| 19 | Surrogate model-assisted interactive genetic algorithms with individual’s fuzzy and stochastic fitness显示文摘We propose a surrogate model-assisted algorithm by using a directed fuzzy graph to extract a user’s cognition on evaluated individuals in order to alleviate user fatigue in interactive genetic algorithms with an individual’s fuzzy and stochastic fitness. We firstly present an approach to construct a directed fuzzy graph of an evolutionary population according to individuals’ dominance relations, cut-set levels and interval dominance probabilities, and then calculate an individual’s crisp fitness based on the out-degree and in-degree of the fuzzy graph. The approach to obtain training data is achieved using the fuzzy entropy of the evolutionary system to guarantee the credibilities of the samples which are used to train the surrogate model. We adopt a support vector regression machine as the surrogate model and train it using the sampled individuals and their crisp fitness. Then the surrogate model is optimized using the traditional genetic algorithm for some generations, and some good individuals are submitted to the user for the subsequent evolutions so as to guide and accelerate the evolution. Finally, we quantitatively analyze the performance of the presented algorithm in alleviating user fatigue and increasing more opportunities to find the satisfactory individuals, and also apply our algorithm to a fashion evolutionary design system to demonstrate its efficiency. | Xiaoyan SUN, Dunwei GONG (School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou Jiangsu 221116, China) | 2010 | 控制理论与应用(英文版)2010，8，2： | 1 |
| 20 | Robot path planning in uncertain environment using multi-objec- tive particle swarm optimization 显示文摘 | ZHANG Yong GONG Dunwei ZHANG Jianhua | 2013 | Neuro Computing2013，103，1： | 1 |
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