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genetic algorithms Applied to Multi-Class Clustering for Gene Ex-pression Data

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Genomics, Proteomics & Bioinformatics 2003年第4期1卷 279-287页

作者： HaiyanPan JunZhu DanfuHan InstituteofBioinformatics ZhejiangUniversityHangzhou310029China DepartmentofMathematics ZhejiangUniversityHangzhou310027China

A hybrid GA (genetic algorithm)-based clustering (HGACLUS) schema, combining merits of the Simulated Annealing, was described for finding an optimal or near-optimal set of medoids. This schema maximized the clustering success by achieving internal cluster cohesion and external cluster isolation. The performance of HGACLUS and other methods was compared by using simulated data and open microarray gene-expression datasets. HGACLUS was generally found to be more accurate and robust than other methods discussed in this paper by the exact validation strategy and the explicit cluster number.

关键词： genetic algorithms gene expression clustering medoid

genetic algorithms Development for MultiobjectiveDesign Optimization of Compressor Cascade

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Journal of Thermal Science 1999年第3期8卷 158-165页

作者： Jun LI(Venture Laboratory, Graduate School, Kyoto institute of Technology, Matsugasaki, Sakyo-ku, Kyoto606-8585, Japan)Koji Morinishi Nobuyuki Satofuka(Department of Mechanical and System Engineering, Kyoto Institute of Technology, Matsugasaki,Sakyo-ku, Venture Laboratory Graduate School Kyoto Institute of Technology Kyoto Japan Department of Mechanical and System Engineering Kyoto Institute of Technology Kyoto Japan

Aerodynamic optimization design of compressor blade shape is a design challenge at present because itis inherently a multiobjective problem. Thus, multiobjective genetic algorithms based on the multibranch simulated annealing selection and collection of Pareto solutions strategy have been developedand applied to the optimum design of compressor cascade. The present multiobjective design seeks highpressure rise, high flow turning angle and low total pressure loss at a low inlet Mach number. Paretosolutions obtain the better aerodynamic performance of the cascade than the existing Control DiffusionAirfoil. From the Pareto solutions, the decision maker would be able to find a design that satisfies hisdesign goal best. The results indicate that the feasibility of multiobjective genetic algorithms as amultiple objectives optimization tool in the engineering field.

关键词： multiobjective optimization genetic algorithms Pareto optimal set compressor cascade design.

HYDRODYNAMIC OPTIMIZATION DESIGN OF LOW SOLIDITY VANED DIFFUSER FOR A CENTRIFUGAL PUMP USING genetic algorithms

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Journal of Hydrodynamics 2004年第2期16卷 186-193页

作者： LIJun FENGZhen-ping TSUKAMOTOHiroshi SchoolofEnergyandPowerEngineering Xi＇anJiaotongUniversityXi＇an710049Chinae-mail：junli@mail*** DepartmentofBiologicalFunctionsandEngineering KyushuInstituteofTechnologyKitakyushu8048550Japan

This paper presents a hydrodynamic redesign of the conventional vaneddiffuser into the low solidity varied diffuser for the maximum static pressure recovery in acentrifugal pump. A Bezier curve representation for profile description was coupled with ablade-to-blade flow calculation and a real-coded genetic algorithm. A low solidity vaned diffuser of0.89 in solidity was obtained through the present optimum design. Numerical analysis andexperimental test were made to evaluate the hydrodynamic performance of the centrifugal pump withthe designed low solidity vaned diffuser and original vaned diffuser. The obtained resultsdemonstrate that the centrifugal pump with the optimized vaned diffuser has compact size comparedwith the original one while the performance requirements have been met.

关键词： low solidity vaned diffuser centrifugal pump hydrodynamic optimizationdesign genetic algorithms

Generating test data for both path coverage and fault detection using genetic algorithms

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Frontiers of Computer Science 2013年第6期7卷 822-837页

作者： Dunwei GONG Yan ZHANG School of Information and Electrical Engineering China University of Mining and Technology Xuzhou 221116 China School of Technology Mudanjiang Normal University Mudanjiang 157012 China

The aim of software testing is to find faults in a program under test, so generating test data that can expose the faults of a program is very important. To date, current stud- ies on generating test data for path coverage do not perform well in detecting low probability faults on the covered path. The automatic generation of test data for both path coverage and fault detection using genetic algorithms is the focus of this study. To this end, the problem is first formulated as a bi-objective optimization problem with one constraint whose objectives are the number of faults detected in the traversed path and the risk level of these faults, and whose constraint is that the traversed path must be the target path. An evolution- ary algorithm is employed to solve the formulated model, and several types of fault detection methods are given. Finally, the proposed method is applied to several real-world programs, and compared with a random method and evolutionary opti- mization method in the following three aspects： the number of generations and the time consumption needed to generate desired test data, and the success rate of detecting faults. The experimental results confirm that the proposed method can effectively generate test data that not only traverse the target path but also detect faults lying in it.

关键词： software testing path coverage fault detection test data multi-objective optimization genetic algorithms

Understanding the Nature of Predatory Pricing in Large-Scale Market Economy with genetic algorithms

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Journal of Systems Engineering and Electronics 1997年第2期8卷 33-40,43-44页

作者： Chen Shuheng & Ni Chihchi(AIECON Research Group, Department of Economics,National Chengchi University, Taiwan 11623, P. R. China) AI-ECON Research Group Department of Economics National Chengchi University Taiwan 11623 P. R. China Department of Automatic Control Engg. Institute of Systems Engg. Huazhong University of Science and Technology Wuhan 430074 P. R. China

In this paper the nature of predatory pricing is analyzed with genetic algorithms. It is found that, even under the same payoff structure, the results of the coevolution of weak monopolists and entrants are sensitive to the representationof the decisionmaking process. Two representations are studied in this paper. One is the actionbased representation and the other the strategybased representation. The former is to represent a naive mind and the latter is to capture a sophisticated mind. For the actionbased representation, the convergence results are easily obtained and predatory pricing is only temporary in all simulations. However, for the strategybased representation, predatory pricing is not a rare phenomenon and its appearance is cyclical but not regular. Therefore, the snowball effect of a little craziness observed in the experimental game theory wins its support from this representation. Furthermore, the nature of predatory pricing has something to do with the evolution of the sophisticated rather than the naive minds.

关键词： Chainstore game Predatory pricing Evolutionary game genetic algorithms Coevolutionary stability

Family genetic algorithms based on gene exchange and its application

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Journal of Systems Engineering and Electronics 2006年第4期17卷 864-869页

作者： Li Jianhua Ding Xiangqian Wang Sun'an Yu Qing Ocean Univ. of China Qingdao 2660071P.R.China Xi'an Jiaotong Univ. Xi'an 710049 P.R.China

genetic algorithms （GA） are a search techniques based on mechanics of nature selection and have already been successfully applied in many diverse areas. However, increasing samples show that GA＇s performance is not as good as it was expected to be. Criticism of this algorithm includes the slow speed and premature result during convergence procedure. In order to improve the performance, the population size and individuals＇ space is emphatically described. The influence of individuals＇ space and population size on the operators is analyzed. And a novel family genetic algorithm （FGA） is put forward based on this analysis. In this novel algorithm, the optimum solution families closed to quality individuals is constructed, which is exchanged found by a search in the world space. Search will be done in this microspace. The family that can search better genes in a limited period of time would win a new life. At the same time, the best gene of this micro space with the basic population in the world space is exchanged. Finally, the FGA is applied to the function optimization and image matching through several experiments. The results show that the FGA possessed high performance.

关键词： genetic algorithms function optimization image matching population size individual space.

Reliability-Based Optimum Design of a Simple Offshore Platform Based on genetic algorithms

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China Ocean Engineering 1998年第1期13卷 43-52页

作者： Zhang, LY Hu, YC Li, XJ Tianjin Univ Dept Ocean Engn & Naval Architecture Tianjin 300072 Peoples R China

In this paper, the problem of reliability-based optimal design of simple offshore platform is studied, and a nonlinear fatigue damage model based on damage mechanics and genetic algorithms are used in the fatigue reliability optimum design of the structure under stochastic wave load. The fatigue damage model and the yield failure reliability analyzing model are used in the paper. The reliability of the models and the effectiveness of genetic algorithm are shown by the results of optimum design.

关键词： damage mechanics genetic algorithms reliability-based optimum design fatigue reliability

Simultaneous scheduling of machines and automated guided vehicles in flexible manufacturing systems using genetic algorithms

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Journal of Central South University 2011年第5期18卷 1473-1486页

作者： I.A.Chaudhry S.Mahmood M.Shami National University of Sciences & Technology (NUST) IslamabadPAKISTAN

The problem of simultaneous scheduling of machines and vehicles in flexible manufacturing system (FMS) was addressed.A spreadsheet based genetic algorithm (GA) approach was presented to solve the problem.A domain independent general purpose GA was used,which was an add-in to the spreadsheet *** adaptation of the propritary GA software was demonstrated to the problem of minimizing the total completion time or makespan for simultaneous scheduling of machines and vehicles in flexible manufacturing *** results are presented for a benchmark with 82 test problems,which have been constructed by other *** achieved results are comparable to the previous *** proposed approach can be also applied to other problems or objective functions without changing the GA routine or the spreadsheet model.

关键词： automated guided vehicles (AGVs) scheduling job-shop genetic algorithms flexible manufacturing system (FMS) spreadsheet

Parallel Optimization of Program Instructions Using genetic algorithms

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Computers, Materials & Continua 2021年第6期67卷 3293-3310页

作者： Petre Anghelescu partment of Electronics Communications and ComputersUniversity of PitestiPitesti110040Romania

This paper describes an efficient solution to parallelize softwareprogram instructions, regardless of the programming language in which theyare written. We solve the problem of the optimal distribution of a set ofinstructions on available processors. We propose a genetic algorithm to parallelize computations, using evolution to search the solution space. The stagesof our proposed genetic algorithm are: The choice of the initial populationand its representation in chromosomes, the crossover, and the mutation operations customized to the problem being dealt with. In this paper, geneticalgorithms are applied to the entire search space of the parallelization ofthe program instructions problem. This problem is NP-complete, so thereare no polynomial algorithms that can scan the solution space and solve theproblem. The genetic algorithm-based method is general and it is simple andefficient to implement because it can be scaled to a larger or smaller number ofinstructions that must be parallelized. The parallelization technique proposedin this paper was developed in the C# programming language, and our resultsconfirm the effectiveness of our parallelization method. Experimental resultsobtained and presented for different working scenarios confirm the theoreticalresults, and they provide insight on how to improve the exploration of a searchspace that is too large to be searched exhaustively.

关键词： Parallel instruction execution parallel algorithms genetic algorithms parallel genetic algorithms artificial intelligence techniques evolutionary strategies