An evolutionary system for near-regular texture synthesis

Near-regular texture is probably among the most difficult to handle in the texture synthesis area, because the synthesis must preserve the holistic structural property and the local randomness simultaneously. In this paper, motivated by the relationship between a near-regular texture image and an evolutionary system, we propose a novel texture synthesis algorithm. By defining individuals with appropriate attributes and behaviors, we convert the texture synthesis problem to an evolution process of an evolutionary system. It can achieve high-quality synthesized results on a large variety of near-regular textures without any extra overhead for memory and pretreatment, and the speed approaches real-time. Moreover, it can be easily generalized to deal with other kinds of textures.     

An evolutionary approach for worst-case tolerance design

A sliding-level orthogonal differential evolution algorithm with a two-level orthogonal array (SLODEA2OA) is proposed for solving worst-case tolerance design problems. Tolerance affects system performance and leads to violate design constraints. By including a two-level orthogonal array, the proposed SLODEA2OA obtains robust optimal solutions that minimize the impact of parameter variations and that maintain compliance with a comprehensive constraint set. Two design examples are used for performance evaluation of the SLODEA2OA. The first is a 10-variable function, which includes linear, non-linear, quadratic, and polynomial forms to illustrate its general robustness and computational efficiency. The second example is a speed reducer design that involves seven variables and multiple non-linear engineering constraints. The SLODEA2OA is also compared with sliding-level orthogonal differential evolution algorithms with either three-level orthogonal array or two-level full-factorial design. Additionally, performance comparisons confirm that the proposed SLODEA2OA outperforms nature-inspired methods presented in the literature.     

An evolutionary image matching approach

Bee colony optimization (BCO) is a meta-heuristic technique inspired by natural behavior of the bee colony. In this paper, the BCO technique is exploited to tackle the shape matching problem with the aim to find the matching between two shapes represented via sets of contour points. A number of bees are used to collaboratively search the optimal matching using a proposed proximity-regularized cost function. Furthermore, the proposed cost function considers the proximity information of the matched contour points; this is in the contrast to that these contour points are treated independently in the conventional approaches. Experimental results are presented to demonstrate that the proposed approach is able to provide more accurate shape matching than the conventional approaches.     

An evolutionary neural network approach for module orientationproblems

A novel neural network approach called “Evolutionary Neural Network (ENN)” is presented for the module orientation problem. The goal of this NP-complete problem is to minimize the total wire length by flipping circuit modules with respect to their vertical and/or horizontal axes of symmetry. In order to achieve high quality VLSI systems, it is strongly desired to solve the problem as quickly as possible in the design cycle. Based on the concept of the genetic algorithm, the evolutionary initialization scheme on neuron states is introduced so as to provide a high quality solution within a very short time. The performance of ENN is compared with three heuristic algorithms through simulations on 20 examples with up to 500 modules. The results show that ENN can find the best solutions in the shortest time     

An evolutionary approach for the nurse rerostering problem

The personnel scheduler constructs a deterministic personnel roster that determines the line-of-work for each personnel member. When unexpected events disrupt this roster, the feasibility needs to be restored by constructing a new workable roster. The scheduler must reassign the set of employees in order to cover the disrupted shift such that the staffing requirements and the time-related personnel constraints remain satisfied. In this paper, we propose an evolutionary meta-heuristic to solve the nurse rerostering problem. We show that the proposed procedure performs consistently well under many different circumstances. We test different optimisation strategies and compare our procedure with the existing literature on a dataset that is carefully designed in a controlled and varied way.     

Learning texture discrimination masks

A neural network texture classification method is proposed in this paper. The approach is introduced as a generalization of the multichannel filtering method. Instead of using a general filter bank, a neural network is trained to find a minimal set of specific filters, so that both the feature extraction and classification tasks are performed by the same unified network. The authors compute the error rates for different network parameters, and show the convergence speed of training and node pruning algorithms. The proposed method is demonstrated in several texture classification experiments. It is successfully applied in the tasks of locating barcodes in the images and segmenting a printed page into text, graphics, and background. Compared with the traditional multichannel filtering method, the neural network approach allows one to perform the same texture classification or segmentation task more efficiently. Extensions of the method, as well as its limitations, are discussed in the paper     

Time series pattern discovery by a PIP-based evolutionary approach

Time series are an important and interesting research field due to their many different applications. In our previous work, we proposed a time-series segmentation approach by combining a clustering technique, discrete wavelet transformation (DWT) and a genetic algorithm to automatically find segments and patterns from a time series. In this paper, we propose a perceptually important points (PIP)-based evolutionary approach, which uses PIP instead of DWT, to effectively adjust the length of subsequences and find appropriate segments and patterns, as well as avoid some problems that arose in the previous approach. To achieve this, an enhanced suitability factor in the fitness function is designed, modified from the previous approach. The experimental results on a real financial dataset show the effectiveness of the proposed approach.     

An evolutionary game theory based approach for query expansion

Multimedia Tools and Applications - In Information Retrieval (IR) Systems, an essential technique employed to improve accuracy and efficiency is Query Expansion (QE). QE is the technique that...     

An evolutionary approach for automatically extracting intelligible classification rules

The process of automatically extracting novel, useful and ultimately comprehensible information from large databases, known as data mining, has become of great importance due to the ever-increasing amounts of data collected by large organizations. In particular, the emphasis is devoted to heuristic search methods able to discover patterns that are hard or impossible to detect using standard query mechanisms and classical statistical techniques. In this paper an evolutionary system capable of extracting explicit classification rules is presented. Special interest is dedicated to find easily interpretable rules that may be used to make crucial decisions. A comparison with the findings achieved by other methods on a real problem, the breast cancer diagnosis, is performed.     

Texture models and image measures for texture discrimination

The task of texture segmentation is to identify image curves that separate different textures. To segment textured images, one must first be able to discriminate textures. A segmentation algorithm performs texture-discrimination tests at densely spaced image positions, then interprets the results to localize edges. This article focuses on the first stage, texture discrimination.We distinguish between perceptual and physical texture differences: the former differences are those perceived by humans, while the latter, on which we concentrate, are those defined by differences in the processes that create the texture in the scene. Physical texture discrimination requires computing image texture measures that allow the inference of physical differences in texture processes, which in turn requires modeling texture in the scene. We use a simple texture model that describes textures by distributions of shape, position, and color of substructures. From this model, a set of image texture measures is derived that allows reliable texture discrimination. These measures are distributions of overall substructure length, width, and orientation; edge length and orientation; and differences in averaged color. Distributions are estimated without explicitly isolating image substructures. Tests of statistical significance are used to compare texture measures.A forced-choice method for evaluating texture measures is described. The proposed measures provide empirical discrimination accuracy of 84 to 100% on a large set of natural textures. By comparison, Laws' texture measures provide less than 50% accuracy when used with the same texture-edge detector. Finally, the measures can distinguish textures differing in second-order statistics, although those statistics are not explicitly measured.The author was with the Robotics Laboratory, Computer Science Department, Stanford University, Stanford, California 94305. He is now with the Institut National de Recherche en Informatique et en Automatique (INRIA), Sophia-Antipolis, 2004 Route des Lucioles, 06565 Valbonne Cedex, France.     

An evolutionary approach to enhance data privacy

Dissemination of data with sensitive information about individuals has an implicit risk of unauthorized disclosure. Perturbative masking methods propose the distortion of the original data sets before publication, tackling a difficult tradeoff between data utility (low information loss) and protection against disclosure (low disclosure risk). In this paper, we describe how information loss and disclosure risk measures can be integrated within an evolutionary algorithm to seek new and enhanced masking protections for continuous microdata. The proposed technique constitutes a hybrid approach that combines state-of-the-art protection methods with an evolutionary algorithm optimization. We also provide experimental results using three data sets in order to illustrate and empirically evaluate the application of this technique.     

An evolutionary approach for layout optimization of a three-dimensional truss

Layout optimization is defined as the simultaneous optimization of three aspects, i.e. topology, size and geometry. In this paper, a new approach based on evolutionary genetic search is employed to find the optimal design. The proposed approach departs from standard Genetic Algorithms (GA) in that it does not use binary strings in the representation of the possible solutions. The approach is applied to a 2-D benchmark example and a well-known space truss example. This 25-bar-truss is solved by using the proposed approach, with good results. The effect of removing the restriction of area grouping and the free location of joints and supports in the 25-bar truss are also investigated, with and without symmetry. Major drawbacks in finding the optimal solution by using the ground structure approach are overcome in the proposed method.     

An evolutionary approach to complex schema matching

The schema matching problem can be defined as the task of finding semantic relationships between schema elements existing in different data repositories. Despite the existence of elaborated graphic tools for helping to find such matches, this task is usually manually done. In this paper, we propose a novel evolutionary approach to addressing the problem of automatically finding complex matches between schemas of semantically related data repositories. To the best of our knowledge, this is the first approach that is capable of discovering complex schema matches using only the data instances. Since we only exploit the data stored in the repositories for this task, we rely on matching strategies that are based on record deduplication (aka, entity-oriented strategy) and information retrieval (aka, value-oriented strategy) techniques to find complex schema matches during the evolutionary process. To demonstrate the effectiveness of our approach, we conducted an experimental evaluation using real-world and synthetic datasets. The results show that our approach is able to find complex matches with high accuracy, similar to that obtained by more elaborated (hybrid) approaches, despite using only evidence based on the data instances.     

An evolutionary game-theoretic approach to congestion control

This paper investigates a system where a set of users sharing a bottleneck link must choose the transmission rate at which multimedia traffic is received. Users are assumed to be self-regarding and make their decisions with the sole goal of maximizing their perceived quality. We are interested in the dynamic process by which users adapt their data rates and the convergence of this process to equilibria. We propose a novel two-layer model to represent this system: the upper layer is an evolutionary game-theoretic model that captures how users adapt their rates; the lower layer model captures the network performance and the quality perceived by the users. Using the model proposed, we demonstrate analytically and numerically several interesting properties of the system equilibria. In particular, we establish the relationship between system states that have non-negligible steady state probabilities and Nash equilibria of the induced game.     

An approach to in-process surface texture condition monitoring

Surface textures formed in the machining process have a great influence on parts’ mechanical behaviours. Normally, the surface textures are inspected by using the images of the machined and cleaned parts. In this paper, an in-process surface texture condition monitoring approach is proposed. Based on the grey-level co-occurrence matrices, some surface texture image features are extracted to describe the texture characteristics. On the basis of the empirical model decomposition, some sensitive features are also extracted from the vibration signal. The mapping relationship from texture characteristics to texture image features and vibration signal features is found. A back propagation neural network model is built when the signal features and the texture conditions are respectively inputs and outputs. The particle swarm optimization is used to optimise the weights and thresholds of the neural network. Experimental study verifies the approach's effectiveness in monitoring the surface texture conditions during the machining process. The approach's accuracy and robustness are also verified. Then, the surface texture condition can be monitored efficiently during the machining process.     

An adaptive evolutionary approach for real-time vehicle routing and dispatching

The quality of the convergence process in genetic algorithms depends on the specific choice of strategies and combinations of operators. In this paper, we address this problem and introduce an adaptive evolutionary approach that uses a genetic algorithm in an adaptive process. An application of this approach to the dynamic vehicle routing problem with time windows is presented. We compare the adaptive version of a hybrid genetic algorithm with the non-adaptive one with respect to the robustness and the quality of the generated solutions. The results obtained show the ability of our operator combination adaptation approach to produce solutions that are superior to hand-tuning and other adaptive methods with respect to performance sensitivity and robustness.     

An evolutionary approach to cancer chemotherapy scheduling

In this paper, we investigate the employment of evolutionary algorithms as a search mechanism in a decision support system for designing chemotherapy schedules. Chemotherapy involves using powerful anti-cancer drugs to help eliminate cancerous cells and cure the condition. It is given in cycles of treatment alternating with rest periods to allow the body to recover from toxic side-effects. The number and duration of these cycles would depend on many factors, and the oncologist would schedule a treatment for each patient’s condition. The design of a chemotherapy schedule can be formulated as an optimal control problem; using an underlying mathematical model of tumour growth (that considers interactions with the immune system and multiple applications of a cycle-phase-specific drug), the objective is to find effective drug schedules that help eradicate the tumour while maintaining the patient health’s above an acceptable level. A detailed study on the effects of different objective functions, in the quality and diversity of the solutions, was performed. A term that keeps at a minimum the tumour levels throughout the course of treatment was found to produce more regular treatments, at the expense of imposing a higher strain on the patient’s health, and reducing the diversity of the solutions. Moreover, when the number of cycles was incorporated in the problem encoding, and a parsimony pressure added to the objective function, shorter treatments were obtained than those initially found by trial and error.

An evolutionary computation approach for designing mobile ad hoc networks

This paper presents the topological design of ad hoc networks in terms of distances among static nodes and speeds of mobiles nodes. Due to the complexity of the problem and the number of parameters to be considered, a genetic algorithm combined with the simulation environment NS-2 is proposed to find the optimum solution. More specifically, NS-2 provides the fitness function guiding the genetic search. The proposed framework has been tested using a railway scenario in which several static and mobile nodes are interacting. Results show the feasibility of the proposed framework and illustrate the possibility of genetic approach for solving similar application scenarios.     

An effective scheme for image texture classification based on binary local structure pattern

Effectiveness of local binary pattern (LBP) features is well proven in the field of texture image classification and retrieval. This paper presents a more effective completed modeling of the LBP. The traditional LBP has a shortcoming that sometimes it may represent different structural patterns with same LBP code. In addition, LBP also lacks global information and is sensitive to noise. In this paper, the binary patterns generated using threshold as a summation of center pixel value and average local differences are proposed. The proposed local structure patterns (LSP) can more accurately classify different textural structures as they utilize both local and global information. The LSP can be combined with a simple LBP and center pixel pattern to give a completed local structure pattern (CLSP) to achieve higher classification accuracy. In order to make CLSP insensitive to noise, a robust local structure pattern (RLSP) is also proposed. The proposed scheme is tested over three representative texture databases viz. Outex, Curet, and UIUC. The experimental results indicate that the proposed method can achieve higher classification accuracy while being more robust to noise.