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1毕业设计论文学院信息工程学院专业计算机科学技术与应用班级1625姓名徐东2007年4月15日2目录摘要IAbstractII引言1第一章基本遗传算法21.1遗传算法的产生及发展..........................................................31.2基本原理......................................................................31.3遗传算法的特点................................................................31.4基本遗传算法描述..............................................................51.5遗传算法构造流程..............................................................6第二章遗传算法的实现技术62.1编码方法.....................................................................72.1.1二进制编码..............................................................72.1.2格雷码编码..............................................................72.1.3符点数编码..............................................................82.1.4参数编码................................................................82.2适应度函数..................................................................102.3选择算子....................................................................102.4交叉算子....................................................................102.4.1单点交叉算子...........................................................102.4.2双点交叉算子...........................................................112.4.3均匀交叉算子...........................................................112.4.4部分映射交叉...........................................................112.4.5顺序交叉...............................................................122.5变异算子....................................................................122.6运行参数....................................................................122.7约束条件的处理方法..........................................................132.8遗传算法流程图...............................................................14第三章遗传算法在TSP上的应用153.1TSP问题的建模与描述.........................................................153.2对TSP的遗传基因编码方法....................................................163.3针对TSP的遗传操作算子......................................................173.3.1选择算子...............................................................173.3.1.1轮盘赌选择......................................................173.3.1.2最优保存策略选择................................................173.3.2交叉算子...............................................................203.3.2.1单点交叉.........................................................203.3.2.2部分映射交叉.....................................................213.3.3变异算子...............................................................233.4TSP的混和遗传算法............................................................26第四章实例分析274.1测试数据....................................................................2734.2测试结果....................................................................274.3结果分析....................................................................27摘要TSPTravelingSalesmanProblem旅行商问题是一类典型的NP完全问题，遗传算法是解决NP问题的一种较理想的方法。文章首先介绍了基本遗传算法的基本原理、特点及其基本实现技术接着针对TSP问题，论述了遗传算法在编码表示和遗传算子（包括选择算子、交叉算子变异算子这三种算子）等方面的应用情况，分别指出几种常用的编码方法的优点和缺点，并且结合TSP的运行实例详细分析了基本遗传算法的4个运行参数群体大小、遗传算法的终止进化代数、交叉概率、变异概率，对遗传算法的求解结果和求解效率的影响，经过多次的测试设定出了它们一组比较合理的取值。最后，简单说明了混合遗传算法在求解TSP问题中的应用并对遗传算法解决TSP问题的前景提出了展望。关键词TSP遗传算法遗传算子编码4AbstractTSPTravelingSalesmanProblemisatypicalNPcompleteproblemandgeneticalgorithmGAistheperfectmethodforsolvingNPcompleteproblem.Thebasictheories,characteristicsandthebasictechniquesofGAarefirstintroduced.Thentheencodingmodelandgeneticoperatorsincludingselectionoperation,crossoveroperationandmutationoperationaboutGAinsolvingTSParediscussed.Theadvantagesanddisadvantagesofvariousencodingmethodarerespectivelyindicated,andtheapplicationofthethreebasicgeneticoperatorsiselaborated.Accordingtothegivendata,theresultsandefficienciesareinfluencedbyfourparametersinthebasicgeneticalgorithmthesizeofpopulation,terminategeneration,crosserprobabilityandmutationprobability.Adjusttheparameters,runandtryforbetterones.Atlast,theapplicationofhybridgeneticalgorithmisbrieflypresented.Itispointedoutthatabettercrossoverormutationroutinecanbefoundoutwhichretainsthestructurefromtheparentchromosomesandstillendsupwithalegaltourinthechildchromosomes,whichleadstoabettersolutionthaneverbefore.AndtheprospectforthefutureofgeneticalgorithminsolvingTSPismade.KeywordsTSPgeneticalgorithmgeneticoperatorsencoding
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