16012768
Continuous Optimization based-on Boosting Gaussian Mixture Model
A new estimation of distribution algorithm (EDA) based on Gaussian mixture model (GMM) is proposed, in which boosting, an efficient ensemble learning method, is adopted to estimate GMM. By boosting simple GMM with two components, it has the ability of learning the model structure and parameters automatically without any requirement for prior knowledge. Moreover, since boosting can be viewed as a gradient search for a good fit of some objective in function space, the new EDA is time efficient. A set of experiments is implemented to evaluate the efficiency and performance of the new algorithm. The results show that, with a relatively smaller population and less number of generations, the new algorithm can perform as well as compared EDAs in optimizing multimodal functions
29843270
A Self-adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization
Large scale global optimization (LSGO), which is highly needed for many scientific and engineering applications, is a very important and very difficult task in optimization domain. Various algorithms have been proposed to tackle this challenging problem, but the use of estimation of distribution algorithms (EDAs) to it is rare. This chapter aims at investigating the behavior and performances of uni-variate EDAs mixed with different kernel probability densities via fitness landscape analysis. Based on the analysis, a self-adaptive uni-variate EDA with mixed kernels (MUEDA) is proposed. To assess the effectiveness and efficiency of MUEDA, function optimization tasks with dimension scaling from 30 to 1500 are adopted. Compared to the recently published LSGO algorithms, MUEDA shows excellent convergence speed, final solution quality and dimensional scalability.
26519336
Iris Recognition Algorithm Using Modified Log-Gabor Filters
In this paper, we presented an iris recognition algorithm based on modified Log-Gabor filters. The algorithm is similar as the method proposed by Daugman in general procedure while modified Log-Gabor filters are adopted to extract the iris phase information instead of complex Gabor filters used in Daugman's method. The advantage of Log-Gabor filters over complex Gabor filters is the former are strictly bandpass filters and the latter are not. The property of strictly bandpass makes the Log-Gabor filters more suitable to extract the iris phase features regardless of the background brightness. The comparison experiments between complex Gabor filters based methods and the proposed method are also presented in this paper
17592710
Full-Space LDA With Evolutionary Selection for Face Recognition
Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they usually utilize all eigenvectors of null or range subspaces of within-class scatter matrix (Sw). However, experimental results testified that not all the eigenvectors in the full space of S w are positive to the classification performance, some of which might be negative. As far as we know, there have been no effective ways to determine which eigenvectors should be adopted. This paper proposes a new method EDA+Full-space LDA, which takes full advantage of the discriminative information of the null and range subspaces of Sw by selecting an optimal subset of eigenvectors. An estimation of distribution algorithm (EDA) is used to pursuit a subset of eigenvectors with significant discriminative information in full space of Sw middot EDA+Full-space LDA is tested on ORL face image database. Experimental results show that our method outperforms other LDA methods
3197098
A Multi-Objective Hw-sw Co-Synthesis Algorithm Based on Quantum-Inspired Evolutionary Algorithm
Hardware–Software (HW–SW) co-synthesis is one of the key steps in modern embedded system design. Generally, HW–SW co-synthesis is to optimally allocate processors, assign tasks to processors, and schedule the processing of tasks to achieve a good balance among performance, cost, power consumption, etc. Hence, it is a typical multi-objective optimization problem. In this paper, a new multi-objective HW–SW co-synthesis algorithm based on the quantum-inspired evolutionary algorithm (MQEAC) is proposed. MQEAC utilizes multiple quantum probability amplitude vectors to model the promising areas of solution space. Meanwhile, this paper presents a new crossover operator to accelerate the convergence to the Pareto front and introduces a PE slot-filling strategy to improve the efficiency of scheduling. Experimental results show that the proposed algorithm can solve the typical multi-objective co-synthesis problems effectively and efficiently.
9214943
A restart univariate estimation of distribution algorithm: sampling under mixed Gaussian and Lévy probability distribution
A univariate EDA denoted as ldquoLSEDA-glrdquo for large scale global optimization (LSGO) problems is proposed in this paper. Three efficient strategies: sampling under mixed Gaussian and Levy probability distribution, standard deviation control strategy and restart strategy are adopted to improve the performance of classical univariate EDA on LSGO problems. The motivation of such work is to extend EDAs to LSGO domain reasonably. Comparison among LSEDA-gl, EDA with standard deviation control strategy only (EDA-STDC) and similar EDA version ldquocontinuous univariate marginal distribution algorithmrdquo UMDAc is carried out on classical test functions. Based on the general comparison standard, the strengths and weaknesses of the algorithms are discussed. Besides, LSEDA-gl is tested on 7 functions with 100, 500, 1000 dimensions provided in the CECpsila2008 Special Session on LSGO. This work is also expected to provide a comparison result for the CECpsila2008 special session.
26704614
A New Approach to Forecast Crude Oil Price Based on Fuzzy Neural Network
A new oil price forecasting model based on fuzzy neural network, which combings RBF neural network, Markov chain based semi parametric model and wavelet analysis is proposed in this paper. The high degree of prediction accuracy confirms the performance of the model in accurate forecasts, reinforcement learning properties and mapping capabilities.
36833558
Dielectric mismatch effect on coupled impurity states in a freestanding nanowire
We studied the coupled impurity states in a freestanding semiconductor nanowire (NW), within the effective mass approximation and including the effect of the dielectric mismatch, by using finite element method. Bonding and anti-bonding states are found and their energies converge with increasing distance di between the two impurities. The dependence of the binding energy on the wire radius R and the distance di between the two impurities is investigated, and we compare it with the result of a freestanding NW that contains a single impurity.
16775013
A Dynamic Trust Model for the Multi-agent Systems
Internet provides a fertile medium for new breeds of computer viruses. Many people who have access to a wealth of information via Internet are attacked by more computer viruses than they can effectively process. We present a dynamic computer virus detection model that can detect known viruses and previously unknown viruses to prevent information systems from damage. This model is inspired by biological immune systems that protect the body against damage from pathogens. The architecture of this model, the formal definitions of self nonself antigen, antibody, and vaccine gene library are introduced. Furthermore, the evolution of self and nonself, the generation of the antibody, the evolution of the virus vaccine gene, and the detection of the antigen are depicted. Experiment results show that this model has better capacity of self-adaptability and self-learning in detecting unknown viruses than traditional models.
3441918
Effect of Different Coding Patterns on Compressed Frequency Domain Based Universal JPEG Steganalysis
Current steganalytic schemes for JPEG steganography are in favor of extracting features in DCT (Discrete Cosine Transform) domain and/or spatial domain. A recently proposed compressed frequency domain based universal steganalytic algorithm [21] showed concern over the statistics of Huffman compressed stream. The authors claimed that it was very effective in detecting stego images embedded by JPEG steganographic tools, including JPHide, F5 and OutGuess. Even though only several bytes were embedded, the scheme was still able to work, thus demonstrating astonishing steganalysis capability. By carefully controlled studies on the factors which may have impact on the new steganalytic method, we find out the truly cause of the powerfulness of this "payload-independent" steganalyzer. Experimental results reveal that different coding patterns used in cover and stego images, rather than the Avalanche Criterion [24] explained by its authors, have led to great detection efficiency. When the same coding pattern is employed in both cover and stego images, the performance of the newly devised steganalyzer has greatly dropped. Implication from this paper is that we should ensure the difference between the cover and stego images is only caused by data embedding itself in steganography and steganalysis.
29193982
An Improved On-Line Sequential Learning Algorithm for Extreme Learning Machine
This paper presents an efficient online sequential learning algorithm for extreme learning machine, which can learn data one by one. In this algorithm, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. In the online sequence, the algorithm updates the output-layer weights with a Givens QR decomposition based on the orthogonalized least square algorithm. Simulations on benchmark problems demonstrate that the algorithm produces much better generalization performance than another online sequential extreme learning machine algorithm, or sometimes it has good performance than primitive extreme learning machine algorithm.