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genetic algorithm‑Based SOTIF Scenario Construction for Complex Traffic Flow

Automotive Innovation 2023年第4期6卷 531-546页

作者： Shulian Zhao Jianli Duan Siyu Wu Xinyu Gu Chuzhao Li Kai Yin Hong Wang The School of Vehicle and Mobility Tsinghua UniversityBeijingChina Yanshan University QinhuangdaoChina China Automotive Engineering Research Institute Co. LtdChongqingChina Beijing Jiaotong University BeijingChina

The Safety of The Intended Functionality(SOTIF)challenge represents the triggering condition by elements of a specific scenario and exposes the function limitation of an autonomous vehicle(AV),which leads to hazards.As for operationcontent-related features,the scenario is similar to AVs’SOTIF research and development.Therefore,scenario generation is a significant topic for SOTIF verification and validation procedure,especially in the simulation testing of AVs.Thus,in this paper,a well-designed scenario architecture is first defined,with comprehensive scenario elements,to present SOTIF trigger conditions.Then,considering complex traffic disturbance as trigger conditions,a novel SOTIF scenario generation method is developed.An indicator,also known as Scenario Potential Risk,is defined as the combination of the safety control intensity and the prior collision probability.This indicator helps identify critical scenarios in the proposed method.In addition,the corresponding vehicle motion models are established for general straight roads,curved roads,and safety assessment areas.As for the traffic participants’motion model,it is designed to construct the key dynamic events.To efficiently search for critical scenarios with the trigger of complex traffic flow,this scenario is encoded as genes and it is regenerated through selection,mutation,and crossover iteration processes,known as the genetic algorithm(GA).Experimental results show that the GA-based method could efficiently construct diverse and critical traffic scenarios,contributing to the construction of the SOTIF scenario library.

关键词： SOTIF triggers conditions Scenario generation genetic algorithm Complex traffic disturbance

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genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy:Combined machine learning and experimental validation in mice

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BIOCELL 2023年第6期47卷 1253-1263页

作者： WEI LIANG ZONGWEI ZHANG KEJU YANG HONGTU HU QIANG LUO ANKANG YANG LI CHANG YUANYUAN ZENG Division of Nephrology Renmin Hospital of Wuhan UniversityWuhanChina Nephrology and Urology Research Institute of Wuhan University WuhanChina The First College of Clinical Medical Science Three Gorges UniversityYichangChina School of Life Science and Engineering Southwest Jiaotong UniversityChengduChina

Background:Diabetic nephropathy(DN)is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide.Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN.Kidney biopsy is the gold standard for diagnosing DN;however,its invasive character is its primary limitation.The machine learning approach provides a non-invasive and specific criterion for diagnosing DN,although traditional machine learning algorithms need to be improved to enhance diagnostic performance.Methods:We applied high-throughput RNA sequencing to obtain the genes related to DN tubular tissues and normal tubular tissues of mice.Then machine learning algorithms,random forest,LASSO logistic regression,and principal component analysis were used to identify key genes(CES1G,CYP4A14,NDUFA4,ABCC4,ACE).Then,the genetic algorithm-optimized backpropagation neural network(GA-BPNN)was used to improve the DN diagnostic model.Results:The AUC value of the GA-BPNN model in the training dataset was 0.83,and the AUC value of the model in the validation dataset was 0.81,while the AUC values of the SVM model in the training dataset and external validation dataset were 0.756 and 0.650,respectively.Thus,this GA-BPNN gave better values than the traditional SVM model.This diagnosis model may aim for personalized diagnosis and treatment of patients with DN.Immunohistochemical staining further confirmed that the tissue and cell expression of NADH dehydrogenase(ubiquinone)1 alpha subcomplex,4-like 2(NDUFA4L2)in tubular tissue in DN mice were decreased.Conclusion:The GA-BPNN model has better accuracy than the traditional SVM model and may provide an effective tool for diagnosing DN.

关键词： Diabetic nephropathy Renal tubule Machine learning Diagnostic model genetic algorithm

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genetic algorithm Combined with the K-Means algorithm:A Hybrid Technique for Unsupervised Feature Selection

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Intelligent Automation & Soft Computing 2023年第9期37卷 2687-2706页

作者： Hachemi Bennaceur Meznah Almutairy Norah Alhussain Computer Science Department Imam Mohammad bin Saud Islamic UniversityRiyadh13318Saudi Arabia

The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational costs.The literature includes much research on feature selection for supervised learning.However,feature selection for unsupervised learning has only recently been studied.Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate.This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS,which combines the genetic algorithm(GA)approach with the classical k-Means algorithm.In the proposed algorithm,a new fitness func-tion is designed in addition to new smart crossover and mutation operators.The effectiveness of this algorithm is demonstrated on various datasets.Fur-thermore,the performance of GAk-MEANS has been compared with other genetic algorithms,such as the genetic algorithm using the Sammon Error Function and the genetic algorithm using the Sum of Squared Error Function.Additionally,the performance of GAk-MEANS is compared with the state-of-the-art statistical unsupervised feature selection techniques.Experimental results show that GAk-MEANS consistently selects subsets of features that result in better classification accuracy compared to others.In particular,GAk-MEANS is able to significantly reduce the size of the subset of selected features by an average of 86.35%(72%–96.14%),which leads to an increase of the accuracy by an average of 3.78%(1.05%–6.32%)compared to using all features.When compared with the genetic algorithm using the Sammon Error Function,GAk-MEANS is able to reduce the size of the subset of selected features by 41.29%on average,improve the accuracy by 5.37%,and reduce the time by 70.71%.When compared with the genetic algorithm using the Sum of Squared Error Function,GAk-MEANS on average is able to reduce the size of the subset of selected features by 15.91%,and improve the accuracy by 9.81%,bu

关键词： genetic algorithm unsupervised feature selection k-Means clustering

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genetic algorithm Routing Protocol for Mobile Ad Hoc Network

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Computers, Materials & Continua 2021年第7期68卷 941-960页

作者： Raed Alsaqour Saif Kamal Maha Abdelhaq Yazan Al Jeroudi Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia Department of Computer Technology Engineering Iraq University College(IUC)BasraIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University84428RiyadhSaudi Arabia Department of Mechanical Engineering International Islamic University MalaysiaJalan Gombak53100SelangorMalaysia

Mobile ad hoc network(MANET)is a dynamically reconfigurable wireless network with time-variable infrastructure.Given that nodes are highly mobile,MANET’s topology often changes.These changes increase the difficulty in finding the routes that the packets use when they are routed.This study proposes an algorithm called genetic algorithm-based location-aided routing(GALAR)to enhance the MANET routing protocol efficiency.The GALAR algorithm maintains an adaptive update of the node location information by adding the transmitting node location information to the routing packet and selecting the transmitting node to carry the packets to their destination.The GALAR was constructed based on a genetic optimization scheme that considers all contributing factors in the delivery behavior using criterion function optimization.Simulation results showed that the GALAR algorithm can make the probability of packet delivery ratio more than 99%with less network overhead.Moreover,GALAR was compared to other algorithms in terms of different network evaluation measures.The GALAR algorithm significantly outperformed the other algorithms that were used in the study.

关键词： Mobile ad hoc network location-aided routing protocol location information genetic algorithm

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genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings

能源与人工智能(英文)

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能源与人工智能(英文) 2020年第2期2卷 83-100页

作者： X.J.Luo Lukumon O.Oyedele Anuoluwapo O.Ajayi Olugbenga O.Akinade Juan Manuel Davila Delgado Hakeem A.Owolabi Ashraf Ahmed Big Data Enterprise and Artificial Intelligence Laboratory(Big-DEAL) University of the West of England(UWE)Frenchay CampusBristolUnited Kingdom Department of Civil and Environmental Engineering Brunel University LondonLondonUnited Kindom

A genetic algorithm-determined deep feedforward neural network architecture(GA-DFNN)is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United Kingdom.Due to the comprehensive relationship between affecting factors and real-world building electricity consumption,the adoption of multiple hidden layers in the deep neural network(DFNN)algorithm would improve its prediction accuracy.The architecture of a DFNN model mainly refers to its quantity of hidden layers,quantity of neurons in the hidden layers,activation function in each layer and learning process to obtain the connecting weights.The optimal architecture of DFNN model was generally determined through a trial-and-error process,which is an exponential combinatorial problem and a tedious task.To address this problem,genetic algorithm(GA)is adopted to automatically design an optimal architecture with improved generalization ability.One year and six months of measurement data from a campus building is used for training and testing the proposed GA-DFNN model,respectively.To demonstrate the effectiveness of the proposed GA-DFNN prediction model,its prediction performance,including mean absolute percentage error,coefficient of determination,root mean square error and mean absolute error,was compared to the reference feedforward neural network models with single hidden layer,DFNN models with other architecture,random search determined DFNN model,long-short-term-memory model and temporal convolutional network model.The comparison results show that the proposed GA-DFNN predictive model has superior performance than all the reference prediction models,demonstrating the optimization effectiveness of GA and the prediction effectiveness of DFNN model with multiple hidden layers and optimal architecture.

关键词： Prediction Deep learning Feedforward neural network genetic algorithm Electricity consumption

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Towards Exploring Large Molecular Space:An Efficient Chemical genetic algorithm

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Journal of Computer Science & Technology 2022年第6期37卷 1464-1477页

作者：朱健甫郝中楷刘淇阴钰陆承镪黄振亚陈恩红 Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China

Generating molecules with desired properties is an important task in chemistry and pharmacy.An efficient method may have a positive impact on finding drugs to treat diseases like COVID-19.Data mining and artificial intelligence may be good ways to find an efficient method.Recently,both the generative models based on deep learning and the work based on genetic algorithms have made some progress in generating molecules and optimizing the molecule’s properties.However,existing methods have defects in the experimental evaluation standards.These methods also need to be improved in efficiency and performance.To solve these problems,we propose a method named the Chemical genetic algorithm for Large Molecular Space(CALM).Specifically,CALM employs a scalable and efficient molecular representation called molecular matrix.And we design corresponding crossover,mutation,and mask operators inspired by domain knowledge and previous studies.We apply our genetic algorithm to several tasks related to molecular property optimization and constraint molecular optimization.The results of these tasks show that our approach outperforms the other state-of-the-art deep learning and genetic algorithm methods,where the z tests performed on the results of several experiments show that our method is more than 99%likely to be significant.At the same time,based on the experimental results,we point out the defects in the experimental evaluation standard which affects the fair evaluation of all previous work.Avoiding these defects helps to objectively evaluate the performance of all work.

关键词： data mining molecular generation genetic algorithm drug discovery artificial intelligence

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A genetic-algorithm-Based Information Evolution Model for Social Networks

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China Communications 2016年第12期13卷 234-249页

作者： Yanan Wang Xiuzhen Chen Jianhua Li Wanyu Huang Shanghai Jiao Tong University Shanghai 200240 China

the existing information diffusion models focus on analyzing the spatial distribution of certain pieces of messages in social networks. However, these conventional models ignored another important characteristic of diffusion: gradually changing of message contents due to the ‘new' and ‘comment' mechanisms. A novel genetic-algorithm-based information evolution model is proposed to reproduce both the diffusion and development process of information in social networks. This model firstly proposes a five-tuple to represent three types of topics: independent, competitive and mutually exclusive. Furthermore, it adopts mutation operator and forms new crossover and mutation rules to simulate four typical interactions between individuals, which bring the advantage of reproducing the information evolution process in both popularity and content.A series of experiments tested on public datasets demonstrate that: 1) independent and competitive topics of information rarely affect each other while mutually exclusive topics significantly suppress the diffusion processes of each other; 2) lower mutation probability leads to decreasing of final information amount. The experimental results show that our evolution model is more reasonable and feasible in demonstrating the evolution of information in social networks.

关键词： social network information evolution genetic algorithm mutation five-tuple prolog

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Performance Improvement of Two-Phase Steam Ejector based on CFD Techniques,Response Surface Methodology and genetic algorithm

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Journal of Thermal Science 2024年第2期33卷 675-695页

作者： ZHANG Hao ZHAO Hongxia ZHANG Xiuao ZENG Hongxuan DENG Jianqiang School of Energy and Power Engineering Shandong UniversityJi’nan250061China School of Chemical Engineering and Technology Xi’an Jiaotong UniversityXi’an710049China

The steam ejector is a crucial component in the waste heat recovery system.Its performance determines the amount of recovered heat and system efficiency.However,poor ejector performance has always been the main bottleneck for system applications.Therefore,this study proposes an optimization methodology to improve the steam ejector's performance by utilizing computational fluid dynamics(CFD) techniques,response surface methodology(RSM),and genetic algorithm(GA).Firstly,a homogeneous equilibrium model(HEM) was established to simulate the two-phase flow in the steam ejector.Then,the orthogonal test was presented to the screening of the key decision variables that have a significant impact on the entrainment ratio(ER).Next,the RSM was used to fit a response surface regression model(RSRM).Meanwhile,the effect of the interaction of geometric parameters on the performance of the steam ejector was revealed.Finally,GA was employed to solve the RSRM's global optimal ER value.The results show that the RSRM exhibits a good fit for ER(R^(2)=0.997).After RSM and GA optimization,the maximum ejector efficiency is 27.94%,which is 48.38% higher than the initial ejector of 18.83%.Furthermore,the optimized ejector efficiency is increased by 46.4% on average under off-design conditions.Overall,the results reveal that the combination of CFD,RSM,and GA presents excellent reliability and feasibility in the optimization design of a two-phase steam ejector.

关键词： two-phase steam ejector optimization methodology CFD techniques response surface methodology genetic algorithm homogeneous equilibrium model

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Data Fusion with genetic algorithm Based Lifetime Prediction for Dependable Multi-Processor System-on-Chips

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Tsinghua Science and Technology 2023年第6期28卷 1041-1049页

作者： Yong Zhao Longkun Guo Xiaoyan Zhang NXP Semiconductor Eindhoven 5656the Netherlands School of Math and Statistics Fuzhou UniversityFuzhou 350108China School of Mathematical Science and Institute of Mathematics Nanjing Normal UniversityNanjing 210023China

With the prevalence of big-data technology,intricate,nanoscale Multi-Processor System-on-Chips(MP-SoCs)have been used in various safety-critical applications.However,with no extra countermeasures taken,this widespread use of MP-SoCs can lead to an undesirable decrease in their dependability.This study presents a promising approach using a group of Embedded Instruments(EIs)inside a processor core for health monitoring.Multiple health monitoring datasets obtained from the employed EIs are sampled and collated via the implemented experiment and thereafter used for conducting its remaining useful lifetime prognostics.This enables MP-SoCs to undertake preventive self-repair,thus realizing a zero mean downtime system and ensuring improved dependability.In addition,a principal component analysis based algorithm is designed for realizing the EI data fusion.Subsequently,a genetic algorithm based degradation optimization is employed to create a lifetime prediction model with respect to the processor.

关键词： data fusion genetic algorithm lifetime prediction health monitor multi-core System-on-Chips(SoCs) embedded instruments

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A Length-Adaptive Non-Dominated Sorting genetic algorithm for Bi-Objective High-Dimensional Feature Selection

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1834-1844页

作者： Yanlu Gong Junhai Zhou Quanwang Wu MengChu Zhou Junhao Wen College of Computer Science Chongqing UniversityChongqing 400044China IEEE Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China

As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected features.Evolutionary computing(EC)is promising for FS owing to its powerful search capability.However,in traditional EC-based methods,feature subsets are represented via a length-fixed individual encoding.It is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training time.This work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional FS.In LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space adaptively.Moreover,a dominance-based local search method is employed for further improvement.The experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.

关键词： Bi-objective optimization feature selection(FS) genetic algorithm high-dimensional data length-adaptive

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