By Topic

Computational Intelligence and Security (CIS), 2011 Seventh International Conference on

Date 3-4 Dec. 2011

Filter Results

Displaying Results 1 - 25 of 359
  • [Front cover]

    Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (2026 KB)  
    Freely Available from IEEE
  • [Title page i]

    Page(s): i
    Save to Project icon | Request Permissions | PDF file iconPDF (33 KB)  
    Freely Available from IEEE
  • [Title page iii]

    Page(s): iii
    Save to Project icon | Request Permissions | PDF file iconPDF (89 KB)  
    Freely Available from IEEE
  • [Copyright notice]

    Page(s): iv
    Save to Project icon | Request Permissions | PDF file iconPDF (107 KB)  
    Freely Available from IEEE
  • Table of contents

    Page(s): v - xxvi
    Save to Project icon | Request Permissions | PDF file iconPDF (205 KB)  
    Freely Available from IEEE
  • Preface

    Page(s): xxvii
    Save to Project icon | Request Permissions | PDF file iconPDF (69 KB)  
    Freely Available from IEEE
  • Conference organization

    Page(s): xxviii
    Save to Project icon | Request Permissions | PDF file iconPDF (60 KB)  
    Freely Available from IEEE
  • Program Committee

    Page(s): xxix - xxx
    Save to Project icon | Request Permissions | PDF file iconPDF (68 KB)  
    Freely Available from IEEE
  • Reviewers

    Page(s): xxxi
    Save to Project icon | Request Permissions | PDF file iconPDF (65 KB)  
    Freely Available from IEEE
  • A Cloud Model Based Computational Intelligence Algorithm for Parameter Identification of Chaotic Systems

    Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (199 KB) |  | HTML iconHTML  

    Based on the properties of the cloud model on the process of transforming a qualitative concept to a set of quantitative numerical values, an adaptive computational intelligence optimization algorithm is proposed by analyzing the correspondence between search characteristics and cloud models. In the proposed algorithm, the feature parameters of solution sets are created by a multidimensional backward cloud generator, and then adaptively adjusted based on the change of the elite solution candidates. The result is then used by a forward cloud generator to produce the solution set of next generation. No any search parameters are predefined, and, no matter what the initial solution set is, the whole system can adaptively search for solutions to various complicated optimization problems. Two illustrative examples of parameter identification for Lorenz and Chen chaotic systems are given with the aid of an appropriate evaluation function. Numerical simulation and comparisons with the other two existing algorithms demonstrate the effectiveness and feasibility of the proposed algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Genetic Algorithm Based on a New Fitness Function for Constrained Optimization Problem

    Page(s): 6 - 9
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (160 KB) |  | HTML iconHTML  

    According to the characteristics of constrained optimization problem, a new approach based on a new fitness function is presented to handle constrained optimization problems. The primary features of the algorithm proposed are as follows. Inspired by the smooth function technique, a new fitness function is designed which can automatically search potential solutions. In order to make the fitness function work well, a special technique which keeps a certain number of feasible solutions is also used. In addition, new genetic operators are proposed to enhance the proposed algorithm, i.e., crossover operator and mutation operator are designed according to whether the parent solution is a feasible solution or not. Also, to accelerate the algorithm convergence speed, one dimensional search scheme is incorporated into the crossover operator. At last, the computer simulation demonstrates the effectiveness of the proposed algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Genetic Algorithm Based on Duality for Linear Bilevel Programming Problems

    Page(s): 10 - 14
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (157 KB) |  | HTML iconHTML  

    Linear bilevel programming problem, as a NP-hard problem, is the linear version of bilevel programming, in this paper we design an efficient algorithm for solving this kind of problems by combining genetic algorithm with enumeration procedure of extreme points. First, based on the duality principle, the follower problem is replaced by the prime-dual conditions, and the original problem is transformed into an equivalent single-level programming in which all functions are linear except for one constraint. Then, the bases of the duality problem are considered as individuals. For each selected individual (base), the nonlinear constraint can be simplified to linear one, and some constraints can also be removed from the transformed single-level problem. Hence, the single-level problem is converted into a linear programming. At last, the linear programming is solved and the objective value is taken as the fitness of the individual. The distinguished feature of the algorithm is that the bases of follower's duality problem are searched instead of taking all feasible points into account, which makes the search space smaller. In order to illustrate the efficiency of the algorithm, 4 problems selected from literature are solved, and the results show that the proposed algorithm is efficient and robust. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Heuristic Genetic Process Mining Algorithm

    Page(s): 15 - 19
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (303 KB) |  | HTML iconHTML  

    The current GPM algorithm needs many iterations to get good process models with high fitness which makes the GPM algorithm usually time-consuming and sometimes the result can not be accepted. To mine higher quality model in shorter time, a heuristic solution by adding log-replay based crossover operator and direct/indirect dependency relation based mutation operator is put forward. Experiment results on 25 benchmark logs show encouraging results. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Hybrid Method for Solving Global Optimization Problems

    Page(s): 20 - 23
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (156 KB) |  | HTML iconHTML  

    In this paper, a hybrid descent method, consisting of a genetic algorithm and the filled function method, is proposed. The genetic algorithm is used to locate descent points for previously converged local minima. The combined method has the decent property and the convergence is monotonic. To demonstrate the effectiveness of the proposed hybrid method, several multi-dimensional or non-convex optimization problems are solved. Numerical experiments on benchmark functions with different dimansions denmonstrate that the new algorithm has a more rapid convergence and a higher success rate, and can fine the solutions with higher quality, compared with some other existing similar algorithms, which is consistent with the analysis in theory. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Hybrid Multiobjective Differential Evolution Algorithm Based on Improved e-Dominance

    Page(s): 24 - 28
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (188 KB) |  | HTML iconHTML  

    Differential Evolution(DE) is a kind of simple but powerful evolutionary optimization algorithm with many successful applications. However, it has some weaknesses, especially the slow convergence speed because of weak local search ability in its stochastic search. To overcome the drawback, we first employ the orthogonal design method with quantization technique to generate the initial population, and then incorporate descent direction search of traditional optimization method into DE algorithm to improve the ability of DE in the process of solving multiobjective optimization problems(MOPs), where the descent direction can be found by using the dominance relationship among individuals. On the other hand, to obtain uniformly spread nondominated solutions and avoid deleting the extreme points, an improved ∈-dominance strategy is proposed to update the external nondominated archive. Finally, experiment results confirm the effectiveness of the proposed algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Hybrid Simplex Multi-objective Evolutionary Algorithm Based on Preference Order Ranking

    Page(s): 29 - 33
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (304 KB) |  | HTML iconHTML  

    It could be concluded that all multi-objective evolutionary algorithms draw their strength from two aspects: convergence and diversity. In order to achieve these goals, This paper proposes a hybrid methods that combines GA with simplex search method for multi-objective optimization using preference order ranking. Preference order ranking is used as fitness assignment methodology to accelerate the performance of convergence, especially when the number of objectives is very large. The proposed algorithm also uses three subsets to evolve simultaneous and each subset is divided on the basis of different criterion. In every generation, We carry out simplex-based local search in the first two subsets to achieve faster convergence and better diversity, and the individuals in third subset execute ordinary genetic operator to avoid premature convergence. The proposed algorithm has been compared with other MOEAs in high dimensional problems. The experimental results indicate that our algorithm produces better convergence and diversity performance. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Line-up Competition Differential Evolution Algorithm for the Generalized Assignment Problem

    Page(s): 34 - 37
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (169 KB) |  | HTML iconHTML  

    This paper considers the generalized assignment problem (GAP). It is well-known NP-hard combinatorial optimization problem that is interesting in itself and also appears as a sub problem in other problems of practical importance. Line-up competition Differential Evolution algorithm for the GAP is proposed. The algorithm uses integer-coding structure, and introduces the idea of line-up competition. The experimental results indicates that, compared with other algorithms, this algorithm has the characteristics of quick convergence speed, of falling into local optimization rarely and of powerful optimization-searching ability and this algorithm can solve GAP effectively. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A new filled function algorithm for constrained global optimization problems

    Page(s): 38 - 41
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (171 KB) |  | HTML iconHTML  

    A new filled function is proposed for solving constrained global optimization problems without the coercive condition. The filled function is proposed for escaping the current local minimizer of a constrained global problem by combining the idea of filled function in unconstrained global optimization and the idea of penalty function in constrained optimization. Some numerical results on some typical test problems using algorithm demonstrate the efficiency of the algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A New Improved Particle Swarm Optimization Algorithm

    Page(s): 42 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (165 KB) |  | HTML iconHTML  

    To improve PSO, differential evolution (DEA) and ant colony strategy are involved into PSO algorithm, and new PSO(DAPSO) is presented. Handling the current optimal positions of particles with differential evolution, the detecting and exploitation ability of both PSO and DEA are utilized effectively, and some potential evolution directions are constructed for each particle in PSO, at the same time a strategy is presented to choose which one may be the local best for PSO evolution process just like pheromone table in ant colony algorithm. It is shown by tested with well-known benchmark functions that DAPSO algorithm is better than PSO algorithm with linearly decreasing weight and differential evolution algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Application of the PSO-SVM Model for Credit Scoring

    Page(s): 47 - 51
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (163 KB) |  | HTML iconHTML  

    Consumer credit prediction is considered as an important issue in the credit industry. The credit department often makes decision which depends on intuitive experience with large risk. This study proposed a new model that hybridized the support vector machine (SVM) and particle swarm optimization (PSO) to evaluate the new consumer's credit score. The hybrid model simultaneously optimizes the SVM kernel function parameters and the input feature subset in order to achieve a high accuracy. Two UCI credit data sets are selected as the experimental data to evaluate the prediction performance of the hybrid model. The experimental results are compared with other existing methods which imply that the PSO-SVM model is a promising approach for credit scoring. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Approximate Solution of Fuzzy Sylvester Matrix Equations

    Page(s): 52 - 56
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (251 KB) |  | HTML iconHTML  

    In paper the fuzzy matrix equation AX̃+X̃B=C̃ is investigated. The fuzzy matrix equation is converted to a fuzzy linear system. Then the fuzzy linear system is extended into a crisp system of linear equations. The fuzzy approximate solution of the original fuzzy systems is derived from solving the crisp function linear equations. An example is given to illustrate the proposed method finally. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Energy-efficient Multi-task Scheduling Based on MapReduce for Cloud Computing

    Page(s): 57 - 62
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (260 KB) |  | HTML iconHTML  

    For the problem that the energy efficiency of the cloud computing data center is low, from the point of view of the energy efficiency of the servers, we propose a new energy-efficient multi-task scheduling model based on Google's massive data processing framework. To solve this model, we design a practical encoding and decoding method for the individuals, and construct an overall energy efficiency function of the servers as the fitness value of the individual. Meanwhile, in order to accelerate the convergent speed and enhance the searching ability of our algorithm, a local search operator is introduced. Finally, the experiments show that the proposed algorithm is effective and efficient. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Exploring Centrality-Lethality Rule from Evolution Constraint on Essential Hub and Nonessential Hub in Yeast Protein-Protein Interaction Network

    Page(s): 63 - 65
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (147 KB) |  | HTML iconHTML  

    In the analysis of Protein-Protein Interaction (PPI) network, one intriguing problem is the reason for centrality-lethality rule. To discover the relationship between topological properties and functional features of proteins in PPI network, we classified hub proteins into two types: essential hub and nonessential hub and combined proteins' topological structure and evolutionary rate information together. We found that essential hub proteins tend to be intra-modular hubs and they had higher evolutionary conservation, nonessential hub proteins tend to be inter-modular hubs and they had lower evolutionary conservation. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Glowworm swarm optimization algorithm for solving multi-constrained QoS multicast routing problem

    Page(s): 66 - 70
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (193 KB) |  | HTML iconHTML  

    The glowworm swarm optimization algorithm is used to solve the multi-constrained QoS multicast routing problem and QoS-GSO algorithm is presented. Our test shows that the algorithm can find optimal solution quickly and has better performance than GA and ACO. Furthermore, for the larger multi-constrained QoS multicast routing problem, the QoS-GSO algorithm can also quickly obtain the correct solution, which has good prospects of application. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Hybrid Genetic Algorithm for TSP

    Page(s): 71 - 75
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (234 KB) |  | HTML iconHTML  

    When use simple genetic algorithm for solving the traveling salesman problem, the generated optimal solution is over stochastic and does not consider the neighborhood information in whole search process. In order to reduce the randomness, the paper proposes a hybrid genetic algorithm which based on ant algorithm that making better use of the inspiration information of previous generations. In addition, it adds a local search process so that more useful information is supplied to get the optimal solution. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.