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Genetic and Evolutionary Computing (ICGEC), 2010 Fourth International Conference on

Date 13-15 Dec. 2010

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Displaying Results 1 - 25 of 228
  • [Front cover]

    Page(s): C1
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  • [Title page i]

    Page(s): i
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  • [Title page iii]

    Page(s): iii
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  • [Copyright notice]

    Page(s): iv
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  • Table of contents

    Page(s): v - xix
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  • Preface

    Page(s): xx - xxi
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  • Organizing Committee

    Page(s): xxii - xxvii
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  • list-reviewer

    Page(s): xxviii - xxix
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  • ICGEC 2010 Call for Papers

    Page(s): xxx
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  • Using an Interpolation Method to Make Classification Decision

    Page(s): 1 - 3
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (123 KB) |  | HTML iconHTML  

    Pattern recognition techniques have been widely used. In this paper, we propose an interpolation method for making classification decision (AIMMCD). This method makes an interpolation of the class labels of the patterns of the training set for classifying a new pattern. Compared with conventional pattern recognition techniques, AIMMCD has several advantages. First, when we use AIMMCD to produce the class label for the test pattern, no any training procedure. This means that AIMMCD to be computationally efficient. Second, when AIMMCD predicts the class label for real-world data, it takes into account the information of the class labels of all the patterns from the training set in a reasonable way. Indeed, the algorithm assumes that the training sample close to a pattern will have much influence on the class prediction of this pattern and the training sample far from this pattern will have little influence. Third, though AIMMCD has a very simple form, it is directly applicable to not only two-class problems but also multi-class problems. View full abstract»

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  • Method Study of Forecasting Gas Outburst Based on Rough Set

    Page(s): 4 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (317 KB) |  | HTML iconHTML  

    The gas outburst forecasts model is brought forward in this paper. Firstly, rough set requires discrimination data, considering distributed information of class, and continual condition attributes are discredited adopting information entropy theory. On the basis of that, redundancy attributes are eliminated using rough set reduction algorithm. Reduction attributes and rules are gained. Finally, through instances test the result indicates that forecasts model has higher exact ratio. View full abstract»

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  • An Adaptive Fuzzy Weight PSO Algorithm

    Page(s): 8 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (138 KB) |  | HTML iconHTML  

    In this paper, we propose a novel adaptive fuzzy weight parameter PSO Algorithm (FPSO). In the improved algorithm, the inertia weight reserves its decreasing property after fuzzy treatment, and the position is controlled by fuzzy parameter. Simulations have been done to illustrate that the improved algorithm can regulate global search and local search, and has better search accuracy than the basic PSO and the linear decreasing inertia weight particle swarm optimization (WPSO). View full abstract»

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  • A Best Wavelet Packet Basis Image Compression Algorithm Based on PSO

    Page(s): 11 - 13
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (181 KB) |  | HTML iconHTML  

    A new wavelet packet image compression method is proposed based on PSO algorithm. The PSO is utilized to find out the best wavelet packet basis for image compression. A fitness function is designed in terms of the Mean Square Error (MSE) and the sum of the node entropy. Compared with the global soft threshold compression algorithm provided by Matlab soft, the proposed method exhibits better compression capability. View full abstract»

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  • Cloud Estimation of Distribution Particle Swarm Optimizer

    Page(s): 14 - 17
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (168 KB) |  | HTML iconHTML  

    Cloud estimation of distribution particle swarm optimizer combining PSO and cloud model is introduced. In the algorithm's offspring generation scheme, new particles are generated in the cloud estimation of distribution way or in the PSO way. The innovation of the algorithm is production of cloud particles according to the cloud model theory. The cognitive population obtained during optimization is used to estimate statistical characteristics of good solution regions by backward cloud generator. And then the estimated statistical characteristics are used to produce cloud particles by positive cloud generator. Both the global information from cloud particles and local information from PSO particles are used to guide the further search. The proposed algorithm is applied to some well-known benchmarks. The experimental results show that the algorithm has stronger global search ability than original version of PSO. View full abstract»

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  • High Resolution Sonar Image Segmentation by PSO Based Fuzzy Cluster Method

    Page(s): 18 - 21
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (312 KB) |  | HTML iconHTML  

    In this paper, we manage to use the clustering method realize sonar image segmentation. A particle swarm optimization (PSO) based FCM algorithm (PSO-FCM) is proposed which PSO incorporate with Fuzzy Clustering Method (FCM). The algorithm takes the clustering result of PSO as the initialization of the FCM, and uses fuzzy measures and fuzzy integrals to express the adapt function. At last, the algorithm is applied to the high resolution sonar image segmentation. Segmentation results of FCM and PSO-FCM for several sonar images are compared, which show that the PSO-FCM algorithm has better performance and fit for the sonar image segmentation better than the FCM does. View full abstract»

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  • A Novel Particle Swarm Optimization Algorithm Based on Fuzzy Velocity Updating for Multi-objective Optimization

    Page(s): 22 - 26
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (224 KB) |  | HTML iconHTML  

    A novel particle swarm optimization algorithm for multi-objective optimization (MOO) based on fuzzy velocity updating strategy is developed and implemented in this paper. The proposed algorithm incorporates fuzzy velocity updating strategy, which can characterize to some extent the uncertainty on the true optimality of the global best position, into particle swarm optimization (PSO) so as to avoid the premature convergence and to maintain the swarm diversity. In addition, a crowding distance computation operator for promoting solution diversity and an efficient mutation operator for searching feasible non-dominated solutions are adopted. The proposed algorithm is tested on various benchmark problems taken from the literature and evaluated with standard performance metrics by comparison with NSGA-II. It is found that the proposed algorithm does not have any difficulties in achieving well-spread Pareto optimal solutions with good convergence to true Pareto optimal front. View full abstract»

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  • A Study on the Internal Structure of Family Particle Swarm Optimization

    Page(s): 27 - 30
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (186 KB) |  | HTML iconHTML  

    For the discretization of particles in particle swarm optimization (PSO), we have proposed the family PSO (FPSO) previously. To further study the internal structure of FPSO, this paper defined two kinds of relationships between particles: equal relationship (ER) and generational relationship (GR). FPSO of equal relationship (ER-FPSO) and FPSO of generational relationship (GR-FPSO) were proposed. Simulations for seven benchmark functions demonstrated that the advantage and the effectiveness of ER-FPSO and GR-FPSO. In the experiments, the performances of ER-FPSO and GR-FPSO were also compared. Results indicate GR-FPSO has stronger judgment ability and intelligence. This conceptualization has a great of academic and realistic meaning. View full abstract»

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  • Projective Point Matching Using Modified Particle Swarm Optimization

    Page(s): 31 - 34
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1010 KB) |  | HTML iconHTML  

    A projective point matching algorithm based on modified particle swarm optimization is presented. In the paper, the point matching problem turns into an optimization with two series of parameters, projective transform parameters and correspondent mapping parameters. Firstly, a modified particle swarm optimization (PSO) is introduced and a new rule searching for correspondences, closer point matching rule, is also proposed. We use PSO find the optimal solution. It updates the best geometric transform parameters constantly till find the global best, and in each iteration the closer point matching rule is applied to get the correspondent mapping parameters under the temporary fixed transform parameters. Experiments on both synthetic points and real images demonstrate the algorithm is reliable and validate. View full abstract»

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  • Construct Logic Operation Gates of OR and AND with Multiple Enzymes

    Page(s): 35 - 38
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (249 KB) |  | HTML iconHTML  

    Biomoleculars act as a tool to build a multiple enzyme system based on human metabolic actions to perform basic logic operation connecting OR and AND gates. Three enzymes (invertase, amyloglucosidase, hexokinase) concert as a logic operation part, processing three molecular input signals (sucrose, maltose, and ATP) to produce G6P (glucose-6-phosphate). The other two enzymes glucose-6-phosphate dehydrogenase (G6PD) and salicylate hydroxylase (SHL) compose to function as a signal displayer. Furthermore, we used a latent fluorescent molecule composed of sacylate and fluorescence which can be catalyzed and release fluorescent molecular to produce output signal. According to our experiments, firstly, our design is proved. The typical characteristics of enzyme reactions have been discovered through comparing the expected theoretical cures with the result cures. Secondly, the possibility of applying multiple logic gates into complicate networks has been shown. View full abstract»

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  • Global Optimization Using Meta-Controlled Boltzmann Machine

    Page(s): 39 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (182 KB) |  | HTML iconHTML  

    In this study, a new artificial neuron network model called the meta-controlled Boltzmann machine is introduced. The meta-controlled Boltzmann machine model includes the McCulloch-Pitts model, the Hop field network, and also the Boltzmann machine. The proposed method are applied both diffusion processes and simulated annealing. The convergence proof of the proposed method is shows in this paper. Meta-controlled Boltzmann machine show an ability to solve combinatorial optimization problems better than either Hop field networks or Boltzmann machines. View full abstract»

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  • A Hybrid BPSO Approach for Fuzzy Facility Location Problems with VaR

    Page(s): 43 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (169 KB) |  | HTML iconHTML  

    In this paper, a fuzzy facility location model with Value at Risk (VaR) is proposed, which is a two-stage fuzzy zero-one integer programming. Since the fuzzy parameters of the location problem are continuous fuzzy variables with an infinite support, the computation of VaR is inherently an infinite-dimensional optimization problem, which can not be solved analytically. In order to solve the model, first of all, the objective function VaR is approximated through discretization method of fuzzy variables. Therefore, the original problem is converted to the task of a finite-dimensional optimization. Then, a hybrid heuristic algorithm integrating binary particle swarm optimization (BPSO), simplex algorithm and the approximation approach is designed to solve the location model. Finally, a numerical example is provided. View full abstract»

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  • Diagnosis System Based on Rough Sets Analysis

    Page(s): 47 - 50
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (401 KB) |  | HTML iconHTML  

    Nowadays, power systems play an important role in the whole electric industry. Failures of such systems should result in serious social and economical damages. Therefore, the power systems should be highly reliable. This paper presents the method to build a new type of failure diagnosis system based on rough set theory. The testing data of power systems for their failure conditions are based on experts' evaluations with uncertainty, especially little knowledge and human experiences are available on power system failure diagnosis. The rough set theory plays a vital role in handling them. View full abstract»

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  • Node Distribution Optimization in Mobile Sensor Network Based on Multi-Objective Differential Evolution Algorithm

    Page(s): 51 - 54
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (192 KB) |  | HTML iconHTML  

    In the research on mobile sensor networks, coverage control is one of the most important challenges. For the sensor network constructed by random distribution, better network coverage can be achieved by topology adjustment utilizing mobility of sensor nodes. This paper investigates how to make use of sensor radius adjustment and the mobility of the sensor nodes to improve the sensor network coverage. A node distribution optimization scheme based on multi-objective differential evolution algorithm is proposed. Simulation results show that the proposed scheme can quickly achieve node distribution optimization of a mobile sensor network, increase the effective coverage rate, reduce network redundant coverage and network energy consumption, extend network lifetime, and achieve global optimization of the deployment of the mobile sensor network. View full abstract»

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  • Application of Multi-Attribute Rating Matrix in Cold-start Recommendation

    Page(s): 55 - 58
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (214 KB) |  | HTML iconHTML  

    This recommendation algorithm based on User-Item Rating Matrix is inefficient in the case of cold-start. The Application of Multi-Attribute Rating Matrix (MARM) can solve the problem effectively. The user and item information are analyzed to create their attribute-tables. The user's ratings are mapped to the relevant item attributes and the user's attributes respectively to generate a User Attribute-Item Attribute Rating Matrix (UAIARM). After UAIARM is simplified, MARM will be created. When a new item/user enters into this system, the attributes of new item/user and MARM are matched to find the N users/item with the highest match degrees as the target of the new items or the recommended items. Experiment results validate the cold-start recommendation algorithm based on MARM is efficient. View full abstract»

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  • Shared Service Architecture for Emergency Management System Development

    Page(s): 59 - 62
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (235 KB) |  | HTML iconHTML  

    Emergency management system is a special kind of e-Government system. It needs good data integration and application integration for the existing applications, and it is very effective for a given period of time. This paper discusses how to establish the cross application system and rapid integration through the shared service architecture based on Web Service. The architecture has important reference significance for e-Government integration of cross application systems. View full abstract»

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