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binary genetic algorithm-based pattern LUT for grayscale digital half-toning

SIGNAL IMAGE AND VIDEO PROCESSING 2013年第2期7卷 377-388页

作者： Chatterjee, Arpitam Tudu, Bipan Paul, Kanai Ch Jadavpur Univ Dept Printing Engn Kolkata 700098 India Jadavpur Univ Dept Instrumentat & Elect Engn Kolkata 700098 India

Grayscale digital half-toning is a popular technique to reproduce grayscale images with devices that can support only two levels at output, i.e., black and white. Printers, LCD displays, etc. are some common examples of such devices. Considering 0 and 1 as black and white, respectively, this can be represented as an image-wise binary pattern generation process. The binary patterns are aimed to retain the local tonal and structural characteristics of grayscale image for a faithful illusion of the original grayscale image. Apart from tonal and structural characteristics retention, desired blue-noise characteristics also contribute significantly toward eye pleasant appearance of half-tone images. The paper presents a binary genetic algorithm-based approach to generate such binary patterns through optimizing randomly generated binary strings against a visual cost function. Paper also presents a pattern look-up-table (LUT)-based approach toward conventional clustered dot ordered dithering which is suitable for devices like laser or offset printers that cannot recognize individual pixels. The pattern LUT approach is driven toward green-noise characteristics instead of the blue-noise characteristics. The results obtained with test images are presented pictorially and evaluated through half-tone quality evaluation metrics. The evaluation results and comparison with state-of-art techniques shows the potential of presented technique for practical implementations.

关键词： Digital half-toning binary genetic algorithm Blue-noise characteristics Green-noise characteristics Visual cost function Pattern look-up-table

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binary genetic algorithm for optimal joinpoint detection: Application to cancer trend analysis

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STATISTICS IN MEDICINE 2021年第3期40卷 799-822页

作者： Kim, Seongyoon Lee, Sanghee Choi, Jung-Il Cho, Hyunsoon Yonsei Univ Sch Math & Comp Computat Sci & Engn Seoul South Korea Natl Canc Ctr Grad Sch Canc Sci & Policy Dept Canc Control & Populat Hlth 323 Ilsan Ro Goyang South Korea

The joinpoint regression model (JRM) is used to describe trend changes in many applications and relies on the detection of joinpoints (changepoints). However, the existing joinpoint detection methods, namely, the grid search (GS)-based methods, are computationally demanding, and hence, the maximum number of computable joinpoints is limited. Herein, we developed a genetic algorithm-based joinpoint (GAJP) model in which an explicitly decoupled computing procedure for optimization and regression is used to embed a binary genetic algorithm into the JRM for optimal joinpoint detection. The combinations of joinpoints were represented as binary chromosomes, and genetic operations were performed to determine the optimum solution by minimizing the fitness function, the Bayesian information criterion (BIC) and BIC3. The accuracy and computational performance of the GAJP model were evaluated via intensive simulation studies and compared with those of the GS-based methods using BIC, BIC3, and permutation test. The proposed method showed an outstanding computational efficiency in detecting multiple joinpoints. Finally, the suitability of the GAJP model for the analysis of cancer incidence trends was demonstrated by applying this model to data on the incidence of colorectal cancer in the United States from 1975 to 2016 from the National Cancer Institute's Surveillance, Epidemiology, and End Results program. Thus, the GAJP model was concluded to be practically feasible to detect multiple joinpoints up to the number of grids without requirement to preassign the number of joinpoints and be easily extendable to cancer trend analysis utilizing large datasets.

关键词： binary genetic algorithm cancer incidence joinpoint regression SEER

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Increasing resilience of power systems using intentional islanding;a comparison of binary genetic algorithm and deep learning based method

Increasing resilience of power systems using intentional isl...

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IEEE International Conference on Cyber Security and Resilience (IEEE CSR)

作者： Paradell, Pol Spyridis, Yannis Colet, Alba Ivanova, Anzhelika Luis Dominguez -Garcia, Jose Sesis, Achilleas Efstathopoulos, Georgios Inst Energy Res Catalonia IREC Elect & Power Elect Dept Barcelona Spain 0 Infin Ltd Imperial Off London E6 2JG England

ISBN: (纸本)9781665402859

Several algorithms combining qualitative and quantitative components are currently used for splitting a large interconnected power grid into islands as a measure to provide the best reconfiguration option when a fault appears. The aim of this article is to compare the clustering results of a binary genetic algorithm and a deep learning based method in order to identify the differences and to find in which cases it is rather better applicable each of the techniques.

关键词： intentional islanding binary genetic algorithm deep learning graph convolutional networks power system

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Optimizing Luminance and Chrominance Filtered Channels by binary genetic algorithm (Bga) to Generate Color Halftone Image 3

Optimizing Luminance and Chrominance Filtered Channels by Bi...

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3rd International Conference on Image Information Processing (ICIIP)

作者： Das, P. Paul, K. C. Chatterjee, A. Jadavpur Univ Dept Printing Engn Kolkata W Bengal India

ISBN: (纸本)9781509001484

In the following approach, RGB images were converted into modified CIElab version i.e. linearized color space Yy, Cx, Cz. Here color channel Yy has been fIltered through contrast sensitivity function of luminance channel. Cx and Cz color channel has been fIltered through Chrominance contrast sensitivity function (CSF) model. The fIltered data had been divided into 4*4 blocks. binary pattern for each channel has been compared with original image datasets and error has been calculated. The error has been minimized by the binary genetic algorithm. The halftone pattern has been generated for each channel and it has been superimposed to generate the modified halftone image. The generated half toned image has been compared to the original RGB image visually and mathematically. This work was done using R2007b simulator of MATLAB.

关键词： Digital halftoning ordered dithering luminance filter chrominance filter linearized Yy,Cx,Cz MSE SSIM iterative search based methods simulated annealing binary genetic algorithm Image Quality Assessment

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Intentional Islanding of Electricity Grids Using binary genetic algorithm 2

Intentional Islanding of Electricity Grids Using Binary Gene...

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IEEE 2nd Global Power, Energy and Communication Conference (GPECOM)

作者： Ivanova, Anzhelika Paradell, Pol Luis Dominguez-Garcia, Jose Colet, Alba Inst Energy Res Catalonia IREC Power Syst Grp Barcelona Spain

ISBN: (纸本)9781728162645

The electrical distribution network is a critical infrastructure which needs to be resilient to any disruption (e.g. typical faults, climate-change impacts or cyber-attacks). To do so, some automatic actions for grid protections must be performed leading to risk isolation and service continuation, by taking advantage of SmartGrid technologies for decision making. The aim of this article is to provide a method of self-healing and islanding for the electric grid, when a fault appears, due to an electric problem or not. The presented tool has been developed to provide an optimal configuration of a grid when a problem occurs, with the least amount of changes to the grid and maintaining the power supply to the maximum number of users possible. The tool relies on the binary genetic algorithm to decide the islands. The presented tool has been validated by means of simulations with a modified version of the CIGRE 15 benchmark grid model.

关键词： self-healing clusterization electricity grids binary genetic algorithm resilience

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A Novel Spectral Feature Selection Method Based on binary genetic algorithm for Efficient Detection of Endometrial and Ovarian Cancers: Preliminary Results

A Novel Spectral Feature Selection Method Based on Binary Ge...

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Medical Technologies Congress (TIPTEKNO)

作者： Oumar Djibrillah, Fatime Yuksel, Mehmet Emin Erciyes Univ Inst Sci & Engn Dept Biomed Engn TR-38039 Kayseri Turkey Erciyes Univ Fac Engn Dept Biomed Engn TR-38039 Kayseri Turkey

ISBN: (纸本)9781665454322

Endometrial and ovarian cancers are the most common gynecological cancers. Early and accurate diagnosis of both diseases is essential for minimizing mortality rate as well as treatment costs. In this study, we propose a novel methodology for efficient detection of endometrial and ovarian cancers. The proposed approach is based on processing urine-based Fourier Transform infrared spectroscopy data by using machine learning powered by evolutionary feature selection. We propose a novel spectral feature selection method based on a binary genetic algorithm, which is combined with a number of well-known machine learning methods and applied on spectroscopy data obtained from urine samples. Our results show that the presented approach offers superior performance over other methods in the literature for the same purpose. It can therefore efficiently be used for accurate and automated detection of endometrial and ovarian cancers by utilizing a very small set of spectral features, which provides a significant benefit regarding physical implementation of the proposed pipeline as a biosensor kit.

关键词： Endometrial cancer Ovarian cancer Spectral feature selection binary genetic algorithm Machine Learning Classification

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Adaptive crossover operator based multi-objective binary genetic algorithm for feature selection in classification

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KNOWLEDGE-BASED SYSTEMS 2021年 227卷 107218-107218页

作者： Xue, Yu Zhu, Haokai Liang, Jiayu Slowik, Adam Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Tiangong Univ Tianjin Key Lab Autonomous Intelligent Technol & Tianjin 300387 Peoples R China Koszalin Univ Technol Dept Elect & Comp Sci PL-75453 Koszalin Poland

Feature selection is a key pre-processing technique for classification which aims at removing irrelevant or redundant features from a given dataset. Generally speaking, feature selection can be considered as a multi-objective optimization problem, i.e, removing number of features and improving the classification accuracy. genetic algorithms (GAs) have been widely used for feature selection problems. The crossover operator, as an important technique to search for new solutions in GAs, has a strong impact on the final optimization results. However, many crossover operators are problem-dependent and have different search abilities. Thus, it is a challenge to select the most efficient one to solve different feature selection problems, especially when the nature of feature selection problems is unknown in advance. In order to overcome this challenge, in this paper, a multi-objective binary genetic algorithm integrating an adaptive operator selection mechanism (MOBGA-AOS) is proposed. In MOBGA-AOS, five crossover operators with different search characteristics are used. Each of them is assigned a probability based on the performance in the evolution process. In different phases of evolution, the proper crossover operator is selected by roulette wheel selection according to the probabilities to produce new solutions for the next generation. The proposed algorithm is compared with five well-known evolutionary multi-objective algorithms on ten datasets. The experimental results reveal that MOBGA-AOS is capable of removing a large amount of features while ensuring a small classification error. Moreover, it obtains prominent advantages on large-scale datasets, which demonstrates that MOBGA-AOS is competent to solve high-dimensional feature selection problems. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Feature selection binary genetic algorithm Multi-objective optimization Adaptive operator selection Classification

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Optimal design of broadband non-radiative dielectric guide devices using binary genetic algorithm and 2D-FVFEM

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2022年第4期35卷

作者： Bashir, Tahir Morimoto, Keita Iguchi, Akito Tsuji, Yasuhide Kashiwa, Tatsuya Nishiwaki, Shinji Muroran Inst Technol Informat & Elect Engn 27-1 Mizumoto Cho Muroran Hokkaido 0508585 Japan Kitami Inst Technol Informat & Commun Engn Kitami Hokkaido Japan Kyoto Univ Grad Sch Engn Kyoto Japan

In this paper, a class of broadband non-radiative dielectric (NRD) guide devices based on digital material are presented using optimal design technique. The NRD guides to be discussed here include low crosstalk crossing waveguide, T-branch, 90 degrees-bend, and Z-bend waveguide. To reduce the computational efforts, we employ recently proposed 2D-FVFEM as simulation method. Based on digital material optimization strategy, binary genetic algorithm is implemented to obtain an optimal device structure in the design region. To achieve the desired properties, we introduce a unique one-two symmetric conditions for the structural optimization. Hence, the designed crossing, T-branch, 90 degrees-bend, and Z-bend, respectively, achieve high transmission efficiencies greater than 99.5%, 49.5%:49.5%, 99.4%, and 99.9% at operating frequency 60 GHz, and, furthermore, achieve broadband property as well in the frequency ranges of 58 -64, 58-62, 59-62, and 59-63 GHz. The numerical results of 2D-FVFEM are also verified by 3D-FVFEM. Owing to excellent performances, the proposed NRD guide devices can be useful in a lot of millimeter-wave circuit and system applications.

关键词： binary genetic algorithm full-vectorial finite element method non-radiative dielectric waveguide (NRD guide)

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A New Hybrid Feature Subset Selection Framework Based on binary genetic algorithm and Information Theory

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2019年第3期18卷

作者： Shukla, Alok Kumar Singh, Pradeep Vardhan, Manu Dept Comp Sci & Engn Raipur Madhya Pradesh India

The explosion of the high-dimensional dataset in the scientific repository has been encouraging interdisciplinary research on data mining, pattern recognition and bioinformatics. The fundamental problem of the individual Feature Selection (FS) method is extracting informative features for classification model and to seek for the malignant disease at low computational cost. In addition, existing FS approaches overlook the fact that for a given cardinality, there can be several subsets with similar information. This paper introduces a novel hybrid FS algorithm, called Filter-Wrapper Feature Selection (FWFS) for a classification problem and also addresses the limitations of existing methods. In the proposed model, the front-end filter ranking method as Conditional Mutual Information Maximization (CMIM) selects the high ranked feature subset while the succeeding method as binary genetic algorithm (BGA) accelerates the search in identifying the significant feature subsets. One of the merits of the proposed method is that, unlike an exhaustive method, it speeds up the FS procedure without lancing of classification accuracy on reduced dataset when a learning model is applied to the selected subsets of features. The efficacy of the proposed (FWFS) method is examined by Naive Bayes (NB) classifier which works as a fitness function. The effectiveness of the selected feature subset is evaluated using numerous classifiers on five biological datasets and five UCI datasets of a varied dimensionality and number of instances. The experimental results emphasize that the proposed method provides additional support to the significant reduction of the features and outperforms the existing methods. For microarray data-sets, we found the lowest classification accuracy is 61.24% on SRBCT dataset and highest accuracy is 99.32% on Diffuse large B-cell lymphoma (DLBCL). In UCI datasets, the lowest classification accuracy is 40.04% on the Lymphography using /nearest neighbor (k-NN) and highes

关键词： binary genetic algorithm bioinformatics conditional mutual information maximization k-nearest neighbor microarray

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Optimizing Luminance and Chrominance Filtered Channels by binary genetic algorithm (Bga) to Generate Color Halftone Image

Optimizing Luminance and Chrominance Filtered Channels by Bi...

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International Conference on Image Information Processing

作者： P. DAS K. C. PAUL A. CHATTERJEE Department of Printing Engineering Jadavpur University

ISBN: (纸本)9781509001491

In the following approach, RGB images were converted into modified CIElab version i.e. linearized color space Yy,Cx,Cz. Here color channel Yy has been filtered through contrast sensitivity function of luminance channel. Cx and Cz color channel has been filtered through Chrominance contrast sensitivity function (CSF) model. The filtered data had been divided into 4*4 blocks. binary pattern for each channel has been compared with original image datasets and error has been calculated. The error has been minimized by the binary genetic algorithm. The halftone pattern has been generated for each channel and it has been superimposed to generate the modified halftone image. The generated halftoned image has been compared to the original RGB image visually and mathematically. This work was done using R2007b simulator of MATLAB.

关键词： Digital halftoning Ordered dithering Luminance filter Chrominance filter Linearized Yy Cx Cz MSE SSIM Iterative search based methods Simulated annealing binary genetic algorithm Image Quality Assessment halftone image Mean square error simulated annealing Chromaticity Quality assessment color channel Christmas Island genetic algorithm (GA) red-green-blue colour model Contrast Sensitivity

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