27127935
Automatic Design of Multivariable QFT Control System via Evolutionary Computation
This paper proposes a multi-objective evolutionary automated design methodology for multivariable QFT control systems. Unlike existing manual or convex optimisation based QFT design approaches, the 'intelligent' evolutionary technique is capable of automatically evolving both the nominal controller and pre-filter simultaneously to meet all performance requirements in QFT, without going through the conservative and sequential design stages for each of the multivariable sub-systems. In addition, it avoids the need of manual QFT bound computation and trial-and-error loop-shaping design procedures, which is particularly useful for unstable or non-minimum phase plants for which stabilising controllers maybe difficult to be synthesised. Effectiveness of the proposed QFT design methodology is validated upon a benchmark multivariable system, which offers a set of low-order Pareto optimal controllers that satisfy all the required closed-loop performances under practical constraints.
12902312
Evolutionary computing for knowledge discovery in medical diagnosis
One of the major challenges in medical domain is the extraction of comprehensible knowledge from medical diagnosis data. In this paper, a two-phase hybrid evolutionary classification technique is proposed to extract classification rules that can be used in clinical practice for better understanding and prevention of unwanted medical events. In the first phase, a hybrid evolutionary algorithm (EA) is utilized to confine the search space by evolving a pool of good candidate rules, e.g. genetic programming (GP) is applied to evolve nominal attributes for free structured rules and genetic algorithm (GA) is used to optimize the numeric attributes for concise classification rules without the need of discretization. These candidate rules are then used in the second phase to optimize the order and number of rules in the evolution for forming accurate and comprehensible rule sets. The proposed evolutionary classifier (EvoC) is validated upon hepatitis and breast cancer datasets obtained from the UCI machine-learning repository. Simulation results show that the evolutionary classifier produces comprehensible rules and good classification accuracy for the medical datasets. Results obtained from t-tests further justify its robustness and invariance to random partition of datasets.
26533495
A Tabu-Based Exploratory Evolutionary Algorithm for Multiobjective Optimization
This paper presents an exploratorymultiobjective evolutionary algorithm (EMOEA)that integrates the features of tabu search andevolutionary algorithm for multiobjective (MO)optimization. The method incorporates the taburestriction in individual examination andpreservation in order to maintain the searchdiversity in evolutionary MO optimization,which subsequently helps to prevent the searchfrom trapping in local optima as well as topromote the evolution towards the globaltrade-offs concurrently. In addition, a newlateral interference is presented in the paperto distribute nondominated individuals alongthe discovered Pareto-front uniformly. Unlikemany niching or sharing methods, the lateralinterference can be performed without the needof parameter settings and can be flexiblyapplied in either the parameter or objectivedomain. The features of the proposed algorithmare examined based upon three benchmarkproblems. Experimental results show that EMOEAperforms well in searching and distributingnondominated solutions along the trade-offsuniformly, and offers a competitive behavior toescape from local optima in a noisyenvironment.
207726294
Evolving better population distribution and exploration in evolutionary multi-objective optimization
The aim of multi-objective evolutionary optimization is to minimize the distance between the solution set and the true Pareto front, to distribute the solutions evenly and to maximize the spread of solution set. This paper addresses these issues by presenting two features that enhance the optimization ability of multi-objective evolutionary algorithms. The first feature is a variant of the mutation operator that adapts the mutation rate along the evolution process to maintain a balance between the introduction of diversity and local fine-tuning. In addition, this adaptive mutation operator adopts a new approach to strike a compromise between the preservation and disruption of genetic information. The second feature is an enhanced exploration strategy that encourages the exploration towards less populated areas and hence achieves better discovery of gaps in the generated front. The strategy also preserves non-dominated solutions in the evolving population to achieve a good convergence for the optimization. Comparative studies of some well-known diversity operators, mutation operators and multi-objective evolutionary algorithms are performed on different benchmark problems, which illustrate the effectiveness and efficiency of the proposed features.
19821161
Molecular Dynamics Optimizer
Molecular system possesses two main characteristics that seem to be applicable for the contrary goals of proximity and diversity in multiobjective optimization, namely the converging pressure in potential fields as dictated by the Maxwell-Boltzmann distribution and the inherent drift to a homogenous and uniform equilibrium with maximum entropy, even without any prior knowledge on the geometry and state of the enclosure. Inspired by this association, this paper explores the notion of exploiting molecular motion to solve multiobjective problems. By adapting the algorithmic structure of molecular dynamics, which essentially represents a technique for the computer simulation of molecular motion, a molecular system that is relevant for multiobjective optimization is proposed, known as molecular dynamics optimizer (MDO). The performance of MDO was subsequently compared with other conventional multiobjective optimizers, specifically EA and PSO, and the experimental results demonstrated that MDO is indeed a viable and practical approach for multiobjective optimization.
35739456
A Fast Learning Algorithm Based on Layered Hessian Approximations and the Pseudoinverse
In this article, we present a simple, effective method to learning for an MLP that is based on approximating the Hessian using only local information, specifically, the correlations of output activations from previous layers of hidden neurons. This approach of training the hidden layer weights with the Hessian approximation combined with the training of the final output layer of weights using the pseudoinverse [1] yields improved performance at a fraction of the computational and structural complexity of conventional learning algorithms.
16227528
Fast image retrieval using color-spatial information
Abstract. In this paper, we present an image retrieval system that employs both the color and spatial information of images to facilitate the retrieval process. The basic unit used in our technique is a single-colored cluster, which bounds a homogeneous region of that color in an image. Two clusters from two images are similar if they are of the same color and overlap in the image space. The number of clusters that can be extracted from an image can be very large, and it affects the accuracy of retrieval. We study the effect of the number of clusters on retrieval effectiveness to determine an appropriate value for “optimal'' performance. To facilitate efficient retrieval, we also propose a multi-tier indexing mechanism called the Sequenced Multi-Attribute Tree (SMAT). We implemented a two-tier SMAT, where the first layer is used to prune away clusters that are of different colors, while the second layer discriminates clusters of different spatial locality. We conducted an experimental study on an image database consisting of 12,000 images. Our results show the effectiveness of the proposed color-spatial approach, and the efficiency of the proposed indexing mechanism.
6439034
Efficient Image Retrieval By Color Contents
Images are becoming an important asset and managing them for efficient retrieval poses challenges to the database community. In this paper, we proposed a novel three-tier color index that supports efficient image retrieval by color contents which is important to certain applications, especially when shapes and semantic objects cannot be easily recognized. A prototype painting database system is designed and implemented to demonstrate the effectiveness of the proposed indexing technique. Besides the color index, two other indexes, B+-trees for structured attributes and a signature file for free-text descriptions, were also implemented. As a result, a wide range of queries, both text-based and content-based can be processed efficiently. We also look at existing image database systems based on their query retrieval capabilities.
20194190
An Evaluation of Color-Spatial Retrieval Techniques for Large Image Databases
In a color-spatial retrieval technique, the color information is integrated with the knowledge of the colors' spatial distribution to facilitate content-based image retrieval. Several techniques have been proposed in the literature, but these works have been developed independently without much comparison. In this paper, we present an experimental evaluation of three color-spatial retrieval techniques—the signature-based technique, the partition-based algorithm and the cluster-based method. We implemented these techniques and compare them on their retrieval effectiveness and retrieval efficiency. The experimental study is performed on an image database consisting of 12,000 images. With the proliferation of image retrieval mechanisms and the lack of extensive performance study, the experimental results can serve as guidelines in selecting a suitable technique and designing a new technique.
1614986
Adaptively weighted sub-pattern PCA for face recognition
Adaptively weighted Sub-pattern PCA (Aw-SpPCA) for face recognition is presented in this paper. Unlike PCA based on a whole image pattern, Aw-SpPCA operates directly on its sub-patterns partitioned from an original whole pattern and separately extracts features from them. Moreover, unlike both SpPCA and mPCA that neglect different contributions made by different parts of the human face in face recognition, Aw-SpPCA can adaptively compute the contributions of each part and then endows them to a classification task in order to enhance the robustness to both expression and illumination variations. Experiments on three standard face databases show that the proposed method is competitive.
1118547
Appearance-based eye gaze estimation
We present a method for estimating eye gaze direction, which represents a departure from conventional eye gaze estimation methods, the majority of which are based on tracking specific optical phenomena like corneal reflection and the Purkinje images. We employ an appearance manifold model, but instead of using a densely sampled spline to perform the nearest manifold point query, we retain the original set of sparse appearance samples and use linear interpolation among a small subset of samples to approximate the nearest manifold point. The advantage of this approach is that since we are only storing a sparse set of samples, each sample can be a high dimensional vector that retains more representational accuracy than short vectors produced with dimensionality reduction methods. The algorithm was tested with a set of eye images labelled with ground truth point-of-regard coordinates. We have found that the algorithm is capable of estimating eye gaze with a mean angular error of 0.38 degrees, which is comparable to that obtained by commercially available eye trackers.