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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

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

A Hybrid of Grey Wolf Optimization and genetic algorithm for Optimization of Hybrid Wind and Solar Renewable Energy System

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Journal of the Operations Research Society of China 2022年第4期10卷 749-762页

作者： Diriba Kajela Geleta Mukhdeep Singh Manshahia Department of Mathematics Punjabi UniversityPatialaPunjab147002India Department of Mathematics Madda Walabu UniversityBale Robe4540OromiaEthiopia

In this paper,a hybrid of grey wolf optimization(GWO)and genetic algorithm(GA)has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy system.It was named as hybrid of grey wolf optimization and genetic algorithm(HGWOGA).HGWOGA was applied to this hybrid problem through three procedures.First,the balance between the exploration and the exploitation process was done by grey wolf optimizer algorithm.Then,we divided the population into subpopulation and used the arithmetical crossover operator to utilize the dimension reduction and the population partitioning processes.At last,mutation operator was applied in the whole population in order to refrain from the premature convergence and trapping in local minima.MATLAB code was designed to implement the proposed methodology.The result of this algorithm is compared with the results of iteration method,GWO,GA,artificial bee colony(ABC)and particle swarm optimization(PSO)techniques.The results obtained by this algorithm are better when compared with those mentioned in the text.

关键词： Hybrid renewable energy Optimization Nature-inspired algorithm Grey wolf optimization genetic algorithm

Optimization of fracturing parameters for tight oil production based on genetic algorithm

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Petroleum 2022年第2期8卷 252-263页

作者： Dali Guo Yunwei Kang Zhiyong Wang Yunxiang Zhao Shuguang Li School of Science Southwest Petroleum UniversityChengdu610500China Engineering Technology Research Institute of XDEC Karamay834000China School of Science Civil Aviation Flight University of ChinaDeyang618000China China United Coalbed Methane National Engineering Research Center Co. Ltd.Beijing100095China

It is difficult to determine the main controlling factors of tight oil production.In addition to the problem of uncontrollable prediction accuracy,the numerical prediction model established by the main controlling factors will also make the correctly predicted low production samples lose the value of development.Applying the optimization algorithm with fast convergence speed and global optimization to optimize the controllable parameters in the high-precision numerical prediction model can effectively improve the productivity of low production wells with timeliness,and bring greater economic value while saving development cost.Using PCA-GRA method,the sample weight and the weighted correlation ranking results of parameters affecting tight oil production were obtained.Thereupon then the main controlling factors of tight oil production were determined.Then we set up a BP neural network model with by taking the main controlling factors as input and tight oil production as output.The prediction effect of the network was good and can be put into use.The results of sensitivity analysis showed that the network was stable,and the total fracturing fluid volume had the greatest impact on the production of tight oil.Finally,by using genetic algorithm,we optimized the fracturing parameters of all low production well samples in the field data.Combined with the fracturing parameters of all high production well samples and the optimized fracturing parameters of low production wells,the optimal interval of fracturing parameters was given,which can provide guidance for the field fracturing operation.

关键词： PCA-GRA method Main controlling factors BP neural network genetic algorithm Optimization of fracturing parameters

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

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

A backpropagation neural network improved by a genetic algorithm for predicting the mean radiant temperature around buildings within the long-term period of the near future

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Building Simulation 2022年第3期15卷 473-492页

作者： Yuquan Xie Yasuyuki Ishida Jialong Hu Akashi Mochida Department of Architecture and Building Science Graduate School of EngineeringTohoku UniversityJapan Institute of Technology Tokyu ConstructionJapan

This study aimed to develop a neural network(NN)-based method to predict the long-term mean radiant temperature(MRT)around buildings by using meteorological parameters as training data.The MRT dramatically impacts building energy consumption and significantly affects outdoor thermal comfort.In NN-based long-term MRT prediction,two main restrictions must be overcome to achieve precise results:first,the difficulty of preparing numerous training datasets;second,the challenge of developing an accurate NN model.To overcome these restrictions,a combination of principal component analysis(PCA)and K-means clustering was employed to reduce the training data while maintaining high prediction accuracy.Second,three widely used NN models(feedforward NN(FFNN),backpropagation NN(BPNN),and BPNN optimized using a genetic algorithm(GA-BPNN))were compared to identify the NN with the best long-term MRT prediction performance.The performances of the tested NNs were evaluated using the mean absolute percentage error(MAPE),which was≤3%in each case.The findings indicate that the training dataset was reduced effectively by the PCA and K-means.Among the three NNs,the GA-BPNN produced the most accurate results,with its MAPE being below 1%.This study will contribute to the development of fast and feasible outdoor thermal environment prediction.

关键词： backpropagation neural network principal component analysis mean radiant temperature K-means clustering genetic algorithm long-term prediction

Optimization of a Lobed Mixer with BP Neural Network and genetic algorithm

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Journal of Thermal Science 2023年第1期32卷 387-400页

作者： SONG Yukuan LEI Zhijun LU Xin-Gen XU Gang ZHU Junqiang Key Laboratory of Light-Duty Gas Turbine Institute of Engineering ThermophysicsChinese Academy of SciencesBeijing 100190China School of Aeronautics and Astronautics University of Chinese Academy of SciencesBeijing 100049China

A Sequential Approximate Optimization framework(SAO)for the multi-objective optimization of lobed mixer is established by using the BP neural network and genetic algorithm:the ratio of lobe wavelength to height(η)and the rise angle(α)are selected as the design parameters,and the mixing efficiency,thrust and total pressure loss are the optimization objectives.The CFX commercial solver coupled with the SST turbulence model is employed to simulate the flow field of lobed mixer.A tetrahedral unstructured grid with 5.6 million cells can achieve the similar global results.Based on the response surface approximation model of the lobed mixer,it is necessary to avoid increasing or decreasingαandηat the same time.Instead,theαshould be reduced while theηis appropriately increased,which is conducive to achieving the goal of increasing thrust and reducing losses at the expense of a small decrease in the mixing efficiency.Compared with the normalized method,the non-normalized method with better global optimization accuracy is more suitable for solving the multi-objective optimization problem of the lobed mixer,and its optimal solution(α=8.54°,η=1.165)is the optimal solution of the lobed mixer optimization problem studied in this paper.Compared with the reference lobed mixer,theα,β(the fall angle)and H(lobe height)of the optimal solution are reduced by 0.14°,1.34°and 3.97 mm,respectively,and theηis increased by 0.074;its mixing efficiency is decreased by 4.46%,but the thrust is increased by 2.29%and the total pressure loss is decreased by 0.64%.Downstream of the optimized lobed mixer,the radial scale and peak vorticity of the streamwise voritices decrease with the decreasing lobe height,thereby reducing the mixing efficiency.For the optimized lobed mixer,its low mixing efficiency is the main factor for the decrease of the total pressure loss,but the improvement of the geometric curvature is also conducive to reducing its profile loss.Within the scope of this study,the lobed mixer has an optimal mixing efficiency(ε=74.14%)that m

关键词： lobed mixer optimization BP neural network genetic algorithm jet mixing

Straightforward Guess and Determine Analysis Based on genetic algorithm

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Journal of Systems Science & Complexity 2022年第5期35卷 1988-2003页

作者： CAO Chunping CEN Zhe FENG Xiutao WANG Zhangyi ZHU Yamin Department of Computer Science and Technology University of Shanghai for Science and TechnologyShang-hai 200093China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China School of Science Xi'an Technological UniversityXi'an 710021China

With the development of artificial intelligence,the genetic algorithm has been widely used in many fields.In cryptography,the authors find it is natural to code an individual and design its fitness in a genetic algorithm for a straightforward guess and determine analysis(SGDA,in short).Based on this observation,the authors propose an SGDA based on genetic algorithm.Comparing it with the other three SGDAs based on exhaustive search,MILP method and CPP method respectively,the authors illustrate its effectiveness by three stream ciphers:Small scale SNOW 2.0,medium scale Enocoro-128v2 and large scale Trivium.The results show our method is significantly superior to them,especially for Trivium,the method can find a solution of 165 variables in less than one hour,while the other three methods are not applicable due to its enormous search space of size 2^(619.37).As far as we know,it is a best solution in an SGDA for Trivium so far.

关键词： Enocoro-128v2 genetic algorithm guess and determine analysis SNOW 2.0 trivium