2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)最新文献

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Preliminary theoretical analysis of a local search algorithm to optimize network communication subject to preserving the total number of links 初步从理论上分析了一种局部搜索算法在保持链路总数的前提下优化网络通信
B. Mitavskiy, J. Rowe, C. Cannings
{"title":"Preliminary theoretical analysis of a local search algorithm to optimize network communication subject to preserving the total number of links","authors":"B. Mitavskiy, J. Rowe, C. Cannings","doi":"10.1109/CEC.2008.4630989","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630989","url":null,"abstract":"A variety of phenomena such as world wide web, social or business interactions are modeled by various kinds of networks (such as the scale free or preferential attachment networks). However, due to the model-specific requirements one may want to rewire the network to optimize the communication among the various nodes while not overloading the number of channels (i.e. preserving the number edges). In the current paper we present a formal framework for this problem and a simple heuristic local search algorithm to cope with it. We estimate the expected single-step improvement of our algorithm, establish the ergodicity of the algorithm (i.e. that the algorithm never gets stuck at a local optima) with probability 1) and we also present a few initial empirical results for the scale free networks.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128171750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Self-adaptive multi-objective differential evolution with direction information provided by archived inferior solutions 由存档劣等解提供方向信息的自适应多目标差分进化
Jingqiao Zhang, A. Sanderson
{"title":"Self-adaptive multi-objective differential evolution with direction information provided by archived inferior solutions","authors":"Jingqiao Zhang, A. Sanderson","doi":"10.1109/CEC.2008.4631174","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631174","url":null,"abstract":"We propose a new self-adaptive differential evolution algorithm for multi-objective optimization problems. To address the challenges in multi-objective optimization, we introduce an archive to store recently explored inferior solutions whose difference with the current population is utilized as direction information about the optimum, and also consider a fairness measure in calculating crowding distances to prefer the solutions whose distances to nearest neighbors are large and close to be uniform. As a result, the obtained solutions can spread well over the computed non-dominated front and the front can be moved fast toward the Pareto-optimal front. In addition, the control parameters of the algorithm are adjusted in a self-adaptive manner, avoiding parameter tuning for problems of different characteristics. The proposed algorithm, named JADE2, achieves better or at least competitive results compared to NSGA-II and GDE3 for a set of twenty-two benchmark problems.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125566203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 41
EA-MP: An evolutionary algorithm for a mine planning problem 求解矿山规划问题的进化算法
M. Riff, Teddy Alfaro, Xavier Bonnaire, Carlos Grandón
{"title":"EA-MP: An evolutionary algorithm for a mine planning problem","authors":"M. Riff, Teddy Alfaro, Xavier Bonnaire, Carlos Grandón","doi":"10.1109/CEC.2008.4631344","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631344","url":null,"abstract":"In this paper we introduce an evolutionary algorithm for solving a copper mine planning problem. In the last 10 years this real-world problem has been tackled using linear integer programming and constraint programming. However, because it is a large scale problem, the model must be simplified by relaxing many constraints in order to obtain a near-optimal solution in a reasonable time. We now present an algorithm which takes into account most of the problem constraints and it is able to find better feasible solutions than the approach that has been used until now.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125987600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Data clustering based on complex network community detection 基于复杂网络社区检测的数据聚类
Tatyana B. S. de Oliveira, Liang Zhao, Katti Faceli, A. Carvalho
{"title":"Data clustering based on complex network community detection","authors":"Tatyana B. S. de Oliveira, Liang Zhao, Katti Faceli, A. Carvalho","doi":"10.1109/CEC.2008.4631080","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631080","url":null,"abstract":"Data clustering is an important technique to extract and understand relevant information in large data sets. In this paper, a clustering algorithm based on graph theoretic models and community detection in complex networks is proposed. Two steps are involved in this processing: The first step is to represent input data as a network and the second one is to partition the network into subnetworks producing data clusters. In the network partition stage, each node has a randomly assigned initial angle and it is gradually updated according to its neighbors angle agreement. Finally, a stable state is reached and nodes belonging to the same cluster have similar angles. This process is repeated, each time a cluster is chosen and results in an hierarchical divisive clustering. Simulation results show two main advantages of the algorithm: the ability to detect clusters in different shapes, densities and sizes and the ability to generate clusters with different refinement degrees. Besides of these, the proposed algorithm presents high robustness and efficiency in clustering.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121762432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 18
A sequence cipher producing method based on two-layer ranking Multi-Objective Evolutionary Algorithm 一种基于双层排序多目标进化算法的序列密码生成方法
Kangshun Li, Weifeng Pan, Wensheng Zhang, Zhangxing Chen
{"title":"A sequence cipher producing method based on two-layer ranking Multi-Objective Evolutionary Algorithm","authors":"Kangshun Li, Weifeng Pan, Wensheng Zhang, Zhangxing Chen","doi":"10.1109/CEC.2008.4630794","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630794","url":null,"abstract":"Aiming at designing a high safe and high efficiency cryptosystem, the period of the sequence cipher can not be too long, and the cipher sequence produced should approach random numbers. But the key sequence produced by traditional methods sometimes does not have randomness, which makes insecurity the system using this key sequence. Considering this, in this paper, we take two criteria usually used to evaluate the randomness of a key sequence as two objectives of multi-objective evolutionary algorithm (MOEA), and a new sequence cipher producing method based on two-layer MOEA is proposed (called TLEASCP). Because of TLEASCP is based on the randomness of crossover operator and mutation operator of the high efficient MOEA, the key sequences produced by TLEASCP have the merits of high randomness, chaos and long period.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115822083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A parallel surrogate-assisted multi-objective evolutionary algorithm for computationally expensive optimization problems 求解计算量大的优化问题的并行代理辅助多目标进化算法
Anna Syberfeldt, Henrik Grimm, A. Ng, R. John
{"title":"A parallel surrogate-assisted multi-objective evolutionary algorithm for computationally expensive optimization problems","authors":"Anna Syberfeldt, Henrik Grimm, A. Ng, R. John","doi":"10.1109/CEC.2008.4631228","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631228","url":null,"abstract":"This paper presents a new efficient multi-objective evolutionary algorithm for solving computationally-intensive optimization problems. To support a high degree of parallelism, the algorithm is based on a steady-state design. For improved efficiency the algorithm utilizes a surrogate to identify promising candidate solutions and filter out poor ones. To handle the uncertainties associated with the approximative surrogate evaluations, a new method for multi-objective optimization is described which is generally applicable to all surrogate techniques. In this method, basically, surrogate objective values assigned to offspring are adjusted to consider the error of the surrogate. The algorithm is evaluated on the ZDT benchmark functions and on a real-world problem of manufacturing optimization. In assessing the performance of the algorithm, a new performance metric is suggested that combines convergence and diversity into one single measure. Results from both the benchmark experiments and the real-world test case indicate the potential of the proposed algorithm.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132020646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 37
Multi-objective spam filtering using an evolutionary algorithm 基于进化算法的多目标垃圾邮件过滤
James Dudley, L. Barone, R. L. While
{"title":"Multi-objective spam filtering using an evolutionary algorithm","authors":"James Dudley, L. Barone, R. L. While","doi":"10.1109/CEC.2008.4630786","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630786","url":null,"abstract":"SpamAssassin is a widely-used open source heuristic-based spam filter that applies a large number of weighted tests to a message, sums the results of the tests, and labels the message as spam if the sum exceeds a user-defined threshold. Due to the large number of tests and the interactions between them, defining good weights for SpamAssassin is difficult: moreover, users with different needs may desire different sets of weights to be used. We have built a multi-objective evolutionary algorithm MOSF that evolves weights for the tests in SpamAssassin according to two independent objectives: minimising the number of false positives (legitimate messages mislabeled as spam), and minimising the number of false negatives (spam messages mislabeled as legitimate). We show that MOSF returns a set of solutions offering a range of setups for SpamAssassin satisfying different userspsila needs, and also that MOSF can derive solutions which beat the existing SpamAssassin weights in both objectives simultaneously. Applying these ideas could substantially increase the usefulness of SpamAssassin and similar systems.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130072026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
Multiobjective evolutionary algorithm with constraint handling for aircraft landing scheduling 飞机降落调度的约束处理多目标进化算法
Yuanping Guo, Xianbin Cao, Jun Zhang
{"title":"Multiobjective evolutionary algorithm with constraint handling for aircraft landing scheduling","authors":"Yuanping Guo, Xianbin Cao, Jun Zhang","doi":"10.1109/CEC.2008.4631293","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631293","url":null,"abstract":"Aircraft landing scheduling is a multiobjective optimization problem with lots of constraints, which is difficult to be dealt with by traditional multiobjective evolutionary algorithms with general constraint handling strategies such as constraint-dominate definition. In this paper we pertinently designed an effective constraint handling method, and then presented a multiobjective evolutionary algorithm using the constraint handing method to solve the aircraft landing scheduling problem. Experiments show that our method is able to locate the feasible region in the search space, obtain the jagged Pareto front, and thereby provide efficient schedule for aircraft landing.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130116266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 13
Parameter estimation using a CLPSO strategy 使用CLPSO策略进行参数估计
He-Sheng Tang, W. Zhang, C. Fan, Song-Tao Xue
{"title":"Parameter estimation using a CLPSO strategy","authors":"He-Sheng Tang, W. Zhang, C. Fan, Song-Tao Xue","doi":"10.1109/CEC.2008.4630778","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630778","url":null,"abstract":"As a novel evolutionary computation technique, particle swarm optimization (PSO) has attracted much attention and wide applications for solving complex optimization problems in different fields mainly for various continuous optimization problems. However, it may easily get trapped in a local optimum when solving complex multimodal problems. This paper utilizes an improved PSO by incorporating a comprehensive learning strategy into original PSO to discourage premature convergence, namely CLPSO strategy to estimate parameters of structural systems, which could be formulated as a multi-modal optimization problem with high dimension. Simulation results for identifying the parameters of a structural system under conditions including limited output data and no prior knowledge of mass, damping, or stiffness are presented to demonstrate the effectiveness of the proposed method.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130154497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
On the usefulness of infeasible solutions in evolutionary search: A theoretical study 论进化搜索中不可行解的实用性:一个理论研究
Yang Yu, Zhi-Hua Zhou
{"title":"On the usefulness of infeasible solutions in evolutionary search: A theoretical study","authors":"Yang Yu, Zhi-Hua Zhou","doi":"10.1109/CEC.2008.4630893","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630893","url":null,"abstract":"Evolutionary algorithms (EAs) have been widely used in optimization, where infeasible solutions are often encountered. Some EAs regard infeasible solutions as useless individuals while some utilize infeasible solutions based on heuristic ideas. It is not clear yet that whether infeasible solutions are helpful or not in the evolutionary search. This paper theoretically analyzes that under what conditions in- feasible solutions are beneficial. A sufficient condition and a necessary condition are derived and discussed. Then, the paper theoretically shows that the use of infeasible solutions could change the hardness of the task. For example, an EA-hard problem can be transformed to EA-easy by exploiting infeasible solutions. While, the conditions derived in the paper can be used to judge whether to use infeasible solutions or not.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130163922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 21
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