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 simulation-based optimization approach for dispatching scheduling

An effective scheduling decision is one of the key factors towards improving the efficiency of a system’s performance, particularly in the instance of multiple products thus dispatching rules have been widely used for real-time scheduling because they can provide a very quick and pretty good solution. However, deciding how to select appropriate rules is very difficult. In this paper, we develop evolutionary simulation-based heuristics to construct near-optimal solutions for dispatching rule allocation. Our heuristic is easy to use and gives a manager a useful tool for testing a configuration that can minimize certain performance measures. The optimization heuristics are used to determine priority strategies to maximize the performance of a complex manufacturing system with a large number of different products, along with an overtime that changes with a mix of different process types, including assembly and disassembly operations and with different types of internal and external disturbances. Modeling is carried out using discrete-event simulation. Case study analysis is of a commercial offset printing production system.     

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     

A random walk procedure for texture discrimination

We consider the problem of texture discrimination. Random walks are performed in a plain domain D bounded by an absorbing boundary ? and the absorption distribution is calculated. Measurements derived from such distributions are the features used for discrimination. Both problems of texture discrimination and edge segment detection can be solved using the same random walk approach. The border distributions and their differences with respect to a homogeneous image can classify two different images as having similar or dissimilar textures. The existence of an edge segment is concluded if the boundary distribution for a given window (subimage) differs significantly from the boundary distribution for a homogeneous (uniform grey level) window. The random walk procedure has been implemented and results of texture discrimination are shown. A comparison is made between results obtained using the random walk approach and the first-or second-order statistics, respectively. The random walk procedure is intended mainly for the texture discrimination problem, and its possible application to the edge detection problem (as shown in this paper) is just a by-product.     

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.     

面向纹理合成的块尺寸自动选择算法

在现有的基于块的纹理合成算法中,针对块尺寸需要人工选择从而导致纹理合成质量不确定的问题,提出一种纹理合成中的块尺寸自动选择算法。在纹理样本上按扫描线顺序滑动子块直到遍历所有位置,对子块与纹理样本的直方图进行归一化与均值滤波预处理,然后计算二者直方图的交;在不同子块位置的上述计算结果中,取其最大值作为子块与样本的颜色相似度。针对颜色相似度与块尺寸的近似单调递增关系,采用二分法计算相似度阈值点所对应的横坐标,将其作为纹理合成的块尺寸。多种类型纹理的实验结果表明,该方法自动选择的块尺寸与最佳经验取值范围相吻合。所提方法不仅适用于结构性纹理的合成,而且适用于随机性纹理的合成,能够获得理想的合成结果。     

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 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 evolutionary approach to identify logical components

ContextIdentifying suitable components during the software design phase is an important way to obtain more maintainable software. Many methods including Graph Partitioning, Clustering-based, CRUD-based, and FCA-based methods have been proposed to identify components at an early stage of software design. However, most of these methods use classical clustering techniques, which rely on expert judgment.ObjectiveIn this paper, we propose a novel method for component identification, called SBLCI (Search-Based Logical Component Identification), which is based on GA (genetic algorithm), and complies with an iterative scheme to obtain logical components.MethodSBLCI identifies logical components of a system from its analysis models using a customized GA, which considers cohesion and coupling metrics as its fitness function, and has four novel guided GA operators based on the cohesive component concept. In addition, SBLCI has an iterative scheme in which it initially identifies high-level components in the first iteration. Then, in the next iterations, it identifies low-level sub-components for each identified component in previous iterations.ResultsWe evaluated the effectiveness of SBLCI with three real-world cases. Results revealed that SBLCI is a better alternative for identifying logical components and sub-components in comparison with existing component identification methods.     

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 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 for multi-objective vehicle routing problems with backhauls

The vehicle routing problem (VRP) is an important aspect of transportation logistics with many variants. This paper studies the VRP with backhauls (VRPB) in which the set of customers is partitioned into two subsets: linehaul customers requiring a quantity of product to be delivered, and backhaul customers with a quantity to be picked up. The basic VRPB involves finding a collection of routes with minimum cost, such that all linehaul and backhaul customers are serviced. A common variant is the VRP with selective backhauls (VRPSB), where the collection from backhaul customers is optional. For most real world applications, the number of vehicles, the total travel cost, and the uncollected backhauls are all important objectives to be minimized, so the VRPB needs to be tackled as a multi-objective problem. In this paper, a similarity-based selection evolutionary algorithm approach is proposed for finding improved multi-objective solutions for VRPB, VRPSB, and two further generalizations of them, with fully multi-objective performance evaluation.