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random search and Code Similarity-Based Automatic Program Repair

Journal of Shanghai Jiaotong university(Science) 2023年第6期28卷 738-752页

作者：曹鹤玲刘方正石建树楚永贺邓淼磊 College of Information Science and Engineering Henan University of TechnologyZhengzhou 450001China Henan International Joint Laboratory of Grain Information Processing Henan University of TechnologyZhengzhou 450001China

In recent years,automatic program repair approaches have developed rapidly in the field of software ***,the existing program repair techniques based on genetic programming suffer from requiring verification of a large number of candidate patches,which consume a lot of computational *** this paper,we propose a random search and code similarity based automatic program repair(RSCSRepair).First,to reduce the verification computation effort for candidate patches,we introduce test filtering to reduce the number of test cases and use test case prioritization techniques to reconstruct a new set of test ***,we use a combination of code similarity and random search for patch ***,we use a patch overfitting detection method to improve the quality of *** order to verify the performance of our approach,we conducted the experiments on the Defects4J *** experimental results show that RSCSRepair correctly repairs up to 54 bugs,with improvements of 14.3%,8.5%,14.3%and 10.3%for our approach compared with jKali,Nopol,CapGen and Sim Fix,respectively.

关键词： program repair random search test case prioritization overfitting detection

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Improvement of Pure random search in Global Optimization

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Advances in Manufacturing 2000年第2期4卷 92-95页

作者： Jian-ping1 Peng Ding-hua Shi Shanghai Municipal Commission of Science and Technology Shanghai 200003 China College of Sciences Shanghai University Shanghai 200436 China

In this paper, the improvement of pure random search is studied. By taking some information of the function to be minimized into consideration, the authors propose two stochastic global optimization algorithms. Some numerical experiments for the new stochastic global optimization algorithms are presented for a class of test problems.

关键词： random search global optimization stochastic global optimization algorithm

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Research on stock trend prediction method based on optimized random forest

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 274-284页

作者： Lili Yin Benling Li Peng Li Rubo Zhang School of Computer and Science and Technology Harbin University of Science and TechnologyHarbinChina Department of Computer Science and Engineering Dalian Nationalities UniversityDalianChina

As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical *** in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock *** study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend *** study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random *** the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as ***,the parameter combination of the model is optimized through random parameter *** experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine *** with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.

关键词： ensemble learning finance random forest random search technical indicator

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Predicting non-carcinogenic hazard quotients of heavy metals in pepper (Capsicum annum L.) utilizing electromagnetic waves

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Frontiers of Environmental Science & Engineering 2020年第6期14卷 237-249页

作者： Marzieh Mokarram Hamid Reza Pourghasemi Huichun Zhang Depatment of Range and Watcrshed Managemcent Cllge of Ariculut and Naturel Resures of Darab Shiraz UnivrsityShiraz 71946-84471Iran Depatment of Natural Resources and Enionmental Enginerng Cllge of Agicuture Shiraz UniversityShirz 7141-6586 Iran Depatment of Civil and Environmental Engiccrin Case Westem Reseve Univrsit Cleveland0H 44106USA

Given the tendency of heavy metals to accumulate in soil and plants,the purpose of this study was to determine the *** of Cd,Ni,Pb,and Zn on peppers(*** fruit),grown in contaminated soils in industral *** this purpose,we measured the uptake of the four heavy metals by peppers grown in the heavy metal contaminated soils throughout the four growth stages:two-leaf,growth,fiowering,and fruiting;and calculated various vegetation indices to evaluate the heavy metal contamination *** waves were also applied for analyzing the responses of the target plants to various heavy *** on the relevant spectral bands identified by principal component analysis(PCA)and random search methods,a regression method was then *** determine the most *** bands for estimating,the target hazard quotient(THQ).The THQ was found to be the highest in the plants contaminated by Pb(THQ=62)and Zn(THQ=5.07).The results of PCA and random search indicated that the spectra at the bands of b570,b650,and b760 for Pb,b400 and b880 for Ni,b560 and b880 for Cd,and b560 b910,and b1050 for Zn were the most optimal spectra for assessing ***,in future studies,instead of examining the amount of heavy metals in plants by chemical analysis in the laboratory,the responses of the plants to the electromagnetic waves in the identified bands can be redily investigated in the field based on the established correlations.

关键词： Heavy metals Plants Target Hazard Quotient(THQ) Principal Component Analysis(PCA) random search Electromagnetic wave

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Novel wolf pack optimization algorithm for intelligent medical treatment personalized recommendation

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The Journal of China Universities of Posts and Telecommunications 2018年第6期25卷 44-57页

作者： Wang Dongxing Qian Xu Liu Kang Guan Xinyu School of Mechanical Electronic and Information Engineering China University of Mining and Technology Department of Mechanical Engineering Tsinghua University

To help the people choose a proper medical treatment organizer, this paper proposes an opposition raiding wolf pack optimization algorithm using random search strategy(ORRSS-WPOA) for an adaptive shrinking region. Firstly, via the oppositional raiding method(ORM), each wolf has bigger probability of approaching the leader wolf, which makes the exploration of the wolf pack enhanced as a whole. In another word, the wolf pack is not easy to fall into local optimum. Moreover, random searching strategy(RSS) for an adaptive shrinking region is adopted to strengthen exploitation, which enables any wolf to be more likely to find the optimum in some a given region, so macroscopically the wolf pack is easier to find the global optimal in the given range. Finally, a fitness function was designed to judge the appropriateness between a certain patient and a hospital. The performance of the ORRSS-WPOA was comprehensively evaluated by comparing it with several other competitive algorithms on ten classical benchmark functions and the simulated fitness function aimed to solve the problem mentioned above. Under the same condition, our experimental results indicated the excellent performance of ORRSS-WPOA in terms of solution quality and computational efficiency.

关键词： wolf pack optimization opposition raiding random search adaptive shrinking

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Adaptive Particle Swarm Optimization Algorithm Based on Dynamic Link Matrix and its Application

Adaptive Particle Swarm Optimization Algorithm Based on Dyna...

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第26届中国控制与决策会议

作者： XIA Li-rong LI Run-xue GENG Zhi-qiang School of Information Science and Technology Beijing University of Chemical Technology

ISBN: (纸本)9781479937066

To deal with the problems of topological structure cannot adjust adaptively, easy to trap into the local minimum and diversity losing in traditional particle swarm optimization algorithm, a newly adaptive PSO algorithm based on dynamic link matrix was proposed, which build the neighborhoods though link matrix and divide them into the sub-swarm based on feature clustering. The algorithm can adjust the link probability between particles according to the evolution states of different sub-swarms, which not only realize the topology structure adjustment adaptively and every sub-swarm evolves as their own evolutionary state, but also keep the swarm diversity and better character. The simulation results of the standard benchmark test functions show that the proposed algorithm is better and more effective than the topology of the other proposed PSO algorithm. The feasibility of the method is illustrated with the challenge of the optimization of the selectivity of the first reaction in Kumar.

关键词： Particle Swarm Optimization random search Dynamic link matrix Topology structure Ethylene modeling

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ANew Theoretical Framework forAnalyzing Stochastic Global Optimization Algorithms

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Advances in Manufacturing 1999年第3期3卷 175-180页

作者： SHI Ding hua PENG Jian ping (College of Sciences, Shanghai University) College of Sciences Shanghai University

In this paper, we develop a new theoretical framework by means of the absorbing Markov process theory for analyzing some stochastic global optimization algorithms. Applying the framework to the pure random search, we prove that the pure random search converges to the global minimum in probability and its time has geometry distribution. We also analyze the pure adaptive search by this framework and turn out that the pure adaptive search converges to the global minimum in probability and its time has Poisson distribution.

关键词： Global optimization stochastic global optimization algorithm random search absorbing Markov process

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A New Convergent Algorithm for Online Empirical Risk Minimization

A New Convergent Algorithm for Online Empirical Risk Minimiz...

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第36届中国控制会议

作者： Xinqiang Wang Lei Guo Key Lab.of Systems and Control Institute of Systems ScienceAMSSCAS

ISBN: (纸本)9781538629185

The generalization ability of learning algorithms is the focus of machine learning research,where the empirical risk minimization（ERM） plays an important role when the population distribution of observations is *** of the previous results are mainly based on computational learning theory,which is interested in how many samples are needed to make sure the estimated expected risk satisfies a given accuracy with high *** this paper,we will propose a new algorithm by combining the advantages of both random search and gradient descent algorithms,and show that given an accuracy level of the estimated expected risk,we can generate a hypothesis by our algorithm to guarantee the accuracy with probability 1,and our algorithm will converge in finite *** addition,we will relax the conventional independently and identically distributed（i.i.d.）assumption on the observations to a kind of weakly dependent *** will also provide some simulations to demonstrate our algorithm’s advantages over either random search or gradient descent algorithms.

关键词： ERM random search convergent gradient descent machine learning

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