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Appropriate Combination of Crossover Operator and Mutation Operator in genetic algorithms for the Travelling Salesman Problem

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Computers, Materials & Continua 2024年第5期79卷 2399-2425页

作者： Zakir Hussain Ahmed Habibollah Haron Abdullah Al-Tameem Department of Mathematics and Statistics College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Kingdom of Saudi Arabia Faculty of Computing Universiti Teknologi MalaysiaJohor Bahru81310Malaysia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Kingdom of Saudi Arabia

genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes.It uses a crossover operator to create better offspring chromosomes and thus,converges the population.Also,it uses a mutation operator to explore the unexplored areas by the crossover operator,and thus,diversifies the GA search space.A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution.However,appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem.In this present paper,we aim to study the benchmark traveling salesman problem(TSP).We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances.The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem.The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances.

关键词： Travelling salesman problem genetic algorithms crossover operator mutation operator comprehensive sequential constructive crossover insertion mutation

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Value-Based Test Case Prioritization for Regression Testing Using genetic algorithms

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Computers, Materials & Continua 2023年第1期74卷 2211-2238页

作者： Farrukh Shahzad Ahmed Awais Majeed Tamim Ahmed Khan Department of Software Engineering Bahria UniversityIslamabadCapitalPakistan

Test Case Prioritization(TCP)techniques perform better than other regression test optimization techniques including Test Suite Reduction(TSR)and Test Case Selection(TCS).Many TCP techniques are available,and their performance is usually measured through a metric Average Percentage of Fault Detection(APFD).This metric is value-neutral because it only works well when all test cases have the same cost,and all faults have the same severity.Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results.Therefore,using the right metric for performance evaluation of TCP techniques is very important to get reliable and correct results.In this paper,two value-based TCP techniques have been introduced using genetic Algorithm(GA)including Value-Cognizant Fault Detection-Based TCP(VCFDB-TCP)and Value-Cognizant Requirements Coverage-Based TCP(VCRCB-TCP).Two novel value-based performance evaluation metrics are also introduced for value-based TCP including Average Percentage of Fault Detection per value(APFDv)and Average Percentage of Requirements Coverage per value(APRCv).Two case studies are performed to validate proposed techniques and performance evaluation metrics.The proposed GA-based techniques outperformed the existing state-of-the-art TCP techniques including Original Order(OO),Reverse Order(REV-O),Random Order(RO),and Greedy algorithm.

关键词： Average percentage of fault detection test case prioritization regression testing and value-based testing value-based test case prioritization genetic algorithms

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Generating of Test Data by Harmony Search Against genetic algorithms

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Intelligent Automation & Soft Computing 2023年第4期36卷 647-665页

作者： Ahmed S.Ghiduk Abdullah Alharbi Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceBeni-Suef UniversityBeni-Suef62521Egypt

Many search-based algorithms have been successfully applied in sev-eral software engineering activities.genetic algorithms(GAs)are the most used in the scientiﬁc domains by scholars to solve software testing problems.They imi-tate the theory of natural selection and evolution.The harmony search algorithm(HSA)is one of the most recent search algorithms in the last years.It imitates the behavior of a musician toﬁnd the best harmony.Scholars have estimated the simi-larities and the differences between genetic algorithms and the harmony search algorithm in diverse research domains.The test data generation process represents a critical task in software validation.Unfortunately,there is no work comparing the performance of genetic algorithms and the harmony search algorithm in the test data generation process.This paper studies the similarities and the differences between genetic algorithms and the harmony search algorithm based on the ability and speed ofﬁnding the required test data.The current research performs an empirical comparison of the HSA and the GAs,and then the signiﬁcance of the results is estimated using the t-Test.The study investigates the efﬁciency of the harmony search algorithm and the genetic algorithms according to(1)the time performance,(2)the signiﬁcance of the generated test data,and(3)the adequacy of the generated test data to satisfy a given testing criterion.The results showed that the harmony search algorithm is signiﬁcantly faster than the genetic algo-rithms because the t-Test showed that the p-value of the time values is 0.026algorithms in generating the adequate test data because the t-Test showed that the p-value of theﬁtness values is 0.25>α.

关键词： Harmony search algorithm genetic algorithms test data generation

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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

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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

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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

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Optimal Control of Population Transfer in Three-Level A System with genetic algorithms

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Chinese Physics Letters 2004年第10期21卷 1930-1933页

作者：张向前孙真荣陈果良王祖赓徐至展李儒新 KeyLaboratoryofOpticalandMagneticResonanceSpectroscopy DepartmentofPhysicsandDepartmentofMathematicsEastChinaNormalUniversityShanghai200062 ShanghaiInstituteofOpticsandFineMechanics ChineseAcademyofSciencesShanghai201800

Population transfer in a three-level Λ system is simulated numerically and optimized. Almost complete population transfer from |1 to |3 is achieved by a genetic algorithm while the population in state |2 reached minimum over the entire evolution at the same time. The result shows that the optimal pulse sequence is the well-known stimulated Raman adiabatic passage (STIRAP) scheme. The detuning of pump pulse and Stokes pulse Δp and Δs with the opposite sign and the chirps χp and χs with the same sign are in favour of the complete and robust population transfer for few-cycle laser pulse. Rabi frequencies Ωp and Ωs have insensitive effects on the complete population transfer during a large scope of their ratio when they are large enough.

关键词： genetic algorithms

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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

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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

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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.

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