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International Conference on Advances in Computing, Communications and Informatics ICACCI

作者： Sabharwal, Sangeeta Aggarwal, Manuj NSIT Dept Informat Technol Delhi India

ISBN: (纸本)9781479987924

Combinatorial testing aims at identifying faults that are caused due to interactions of a small number of input parameters. It provides a technique to select a subset of exhaustive test cases covering all the t-way interactions without much loss of the fault detection capability. The test set generated is for a fixed value of t. In this paper, an approach is proposed to generate test set for a system where some variables have higher interaction strength among them as compared to that of the system. Variable Strength Covering Arrays are used for testing such systems. We propose to generate Variable Strength Covering Arrays using multi objective optimization (multi objective genetic algorithms). We attempt to reduce the test set size while covering all the base level interactions of the system and higher strength interactions of its components. Experimental results indicate that the proposed approach generates results comparable to or better in some cases as compared to that of existing approaches.

关键词： Combinatorial Testing Variable Strength Covering Arrays multi objective genetic algorithms Interaction Testing t-way Testing

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Variable Strength Interaction Test Set Generation Using multi objective genetic algorithms

Variable Strength Interaction Test Set Generation Using Mult...

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International Conference on Advances in Computing, Communications and Informatics

作者： Sangeeta Sabharwal Manuj Aggarwal Department of Information Technology NSIT

ISBN: (纸本)9781479987931

关键词： Combinatorial Testing Variable Strength Covering Arrays multi objective genetic algorithms Interaction Testing T-way Testing

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Robust manufacturing system design using multi objective genetic algorithms, Petri nets and Bayesian uncertainty representation

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JOURNAL OF MANUFACTURING SYSTEMS 2013年第2期32卷 315-324页

作者： Sharda, Bikram Banerjee, Amarnath Dow Chem Co USA Freeport TX 77541 USA Texas A&M Univ College Stn TX 77843 USA

Decisions involving robust manufacturing system configuration design are often costly and involve long term allocation of resources. These decisions typically remain fixed for future planning horizons and failure to design a robust manufacturing system configuration can lead to high production and inventory costs, and lost sales costs. The designers need to find optimal design configurations by evaluating multiple decision variables (such as makespan and WIP) and considering different forms of manufacturing uncertainties (such as uncertainties in processing times and product demand). This paper presents a novel approach using multi objective genetic algorithms (GA), Petri nets and Bayesian model averaging (BMA) for robust design of manufacturing systems. The proposed approach is demonstrated on a manufacturing system configuration design problem to find optimal number of machines in different manufacturing cells for a manufacturing system producing multiple products. The objective function aims at minimizing makespan, mean WIP and number of machines, while considering uncertainties in processing times, equipment failure and repairs, and product demand. The integrated multi objective GA and Petri net based modeling framework coupled with Bayesian methods of uncertainty representation provides a single tool to design, analyze and simulate candidate models while considering distribution model and parameter uncertainties. (C) 2013 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： multi objective genetic algorithms Robust design Petri nets Bayesian model averaging

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Automatic detection of non-apneic sleep arousal regions from polysomnographic recordings

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期66卷 102394-102394页

作者： Karimi, Jamileh Asl, Babak Mohammadzadeh Tarbiat Modares Univ Dept Biomed Engn Tehran Iran

A signal processing/machine learning (ML), data-driven approach for classifying targeted sleep arousal regions of polysomnography (PSG) signals is presented focusing on feature subset selection and consensus methods, deploying ensemble techniques. The targeted regions are the regions where RERA and Non-RERA-Non-Apnea events are present. The sensor independent and sensor-based features in time and frequency domain were derived from the PSG signals. To reduce the feature space dimension, a combination of feature selection strategies and a method of rank aggregation was applied to rank the features. Aiming to find a feature set, which conveys the most discriminative information of detection in designated learning models, the Non-Dominated Sorting genetic Algorithm was used as the optimization algorithm. In order to capture the relation between feature vectors across time, a composition of feature vectors was formed. To tackle the unbalanced data problem, several techniques were used and a data fusion strategy stood out. Also, considering a more robust classifier, a metaclassifier was generated using different features, datasets, and classifiers. Finally, the predictions of models generated by bagging techniques and boosting methods were compared. The presented method was developed, validated and tested on the PhysioNet Challenge 2018 training dataset consisting of 994 subjects. The highest performance on 192 test subjects based on the area under precision-recall curve (AUPRC) and the area under receiver operating characteristic (AUROC) curve were 0.465 and 0.927, respectively. This study suggests that automatic detection of RERA and Non-RERA-Non-Apnea sleep arousal regions from biosignals is possible and can be a suitable substitution for PSG.

关键词： Non-apneic sleep arousals multi objective genetic algorithms Non-dominated sorting genetic algorithm Respiratory effort-related arousal Polysomnographic recordings

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Image Enhancement Using multi-objective genetic algorithms

Image Enhancement Using Multi-objective Genetic Algorithms

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3rd International Conference on Pattern Recognition and Machine Intelligence

作者： Bhandari, Dinabandhu Murthy, C. A. Pal, Sankar K. Indian Stat Inst Ctr Soft Comp Res Kolkata 700108 India

ISBN: (纸本)9783642111631

Given an image, there is no unique measure to quantitatively judge the quality of an image enhancement operator. It is also not clear which measure is to be used for the given image. The present work expresses the problem as a multi-objective optimization problem and a methodology has been proposed based on multi-objective genetic algorithm (MOGA). The methodology exploits the effectiveness of MOGA for searching global optimal solutions in selecting an appropriate image enhancement operator.

关键词： Image Enhancement multi objective genetic algorithms Ambiguity Measures

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Automatic calibration of urban drainage model using a novel multi-objective genetic algorithm

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WATER SCIENCE AND TECHNOLOGY 2005年第5期52卷 43-52页

作者： di Pierro, F Djordjevic, S Kapelan, Z Khu, ST Savic, D Walters, GA Univ Exeter Sch Engn Exeter EX4 4QF Devon England

In order to successfully calibrate an urban drainage model, multiple calibration criteria should be considered. This raises the issue of adopting a method for comparing different solutions (parameter sets) according to a set of objectives. Amongst the global optimization techniques that have blossomed in recent years, multi objective genetic algorithms (MOGA) have proved effective in numerous engineering applications, including sewer network modelling. Most of the techniques rely on the condition of Pareto efficiency to compare different solutions. However,as the number of criteria increases, the ratio of Pareto optimal to feasible solutions increases as well. The pitfalls are twofold: the efficiency of the genetic algorithm search worsens and decision makers are presented with an overwhelming number of equally optimal solutions. This paper proposes a new MOGA, the Preference Ordering genetic Algorithm, which alleviates the drawbacks of conventional Pareto-based methods. The efficacy of the algorithm is demonstrated on the calibration of a physically-based, distributed sewer network model and the results are compared with those obtained by NSGA-11, a widely used MOGA.

关键词： automatic calibration multi objective genetic algorithms preference ordering

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