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package com.cet.pojo;import java.util.List;
import java.util.Random;
import com.cet.service.ObjectService;
import com.cet.tool.Algorithm;/*** Test entity.*/public class Test implements java.io.Serializable {/*** 试题*/private static final long serialVersionUID = 1L;// Fieldsprivate Integer id;private String name;private BaseWriting baseWriting;private BaseListening baseListening;private BaseWordunderstand baseWordunderstand;private BaseLongreading baseLongreading;private BaseCarereading baseCarereading1;private BaseCarereading baseCarereading2;private BaseTranslate baseTranslate;private int ismeta;// 遗传算法相关属性private float difficulty = 0;// 试卷难度private double Fitness = 0;// 试卷适应度public int genes[] = new int[7];// 试卷基因序列// Constructors/** default constructor */public Test() {}// 遗传算法相关方法/*** 计算试卷个体难度系数 计算公式： 每题难度*分数求和除总分* * @return*/public float getDifficulty() {if (difficulty == 0) {float _difficulty = (baseWriting.getDifficulty() * (float) 106.5+ baseListening.getDifficulty() * (float) 248.5+ baseTranslate.getDifficulty() * (float) 106.5+ baseWordunderstand.getDifficulty() * (float) 36+ baseLongreading.getDifficulty() * (float) 71+ baseCarereading1.getDifficulty() * (float) 71 + baseCarereading2.getDifficulty()* (float) 71);difficulty = (_difficulty / (float) 710.5);}return difficulty;}/*** 计算个体适应度 公式为：f=1-|EP-P| 其中EP为期望难度系数，P为种群个体难度系数* * @param difficulty*            期望难度系数*/public double getFitness(double EP) {if (Fitness == 0) {Fitness = 1 - Math.abs(EP - getDifficulty());}return Fitness;}/*** 功能：避免重复题型* 检查生成的Test的所有题型是否数据库已经存在，只要Test里有种题型和数据库的某个Test一样，就更换该题型为数据库所有Test里都没有的。* * @param test* @return*/public boolean checkTest() {ObjectService objectService = Algorithm.objectService;Random random = new Random();List<?> notInList = objectService.getObjectNotIn("BaseWriting");if (notInList.size() < 1) {return false;}baseWriting = (BaseWriting) notInList.get(random.nextInt(notInList.size()));notInList = objectService.getObjectNotIn("BaseListening");if (notInList.size() < 1) {return false;}baseListening = (BaseListening) notInList.get(random.nextInt(notInList.size()));notInList = objectService.getObjectNotIn("BaseWordunderstand");if (notInList.size() < 1) {return false;}baseWordunderstand = (BaseWordunderstand) notInList.get(random.nextInt(notInList.size()));notInList = objectService.getObjectNotIn("BaseLongreading");if (notInList.size() < 1) {return false;}baseLongreading = (BaseLongreading) notInList.get(random.nextInt(notInList.size()));notInList = objectService.getObjectNotIn("BaseCarereading");if (notInList.size() < 2) {return false;}int ran = random.nextInt(notInList.size());baseCarereading1 = (BaseCarereading) notInList.get(ran);notInList.remove(ran);baseCarereading2 = (BaseCarereading) notInList.get(random.nextInt(notInList.size()));notInList = objectService.getObjectNotIn("BaseTranslate");if (notInList.size() < 1) {return false;}baseTranslate = (BaseTranslate) notInList.get(random.nextInt(notInList.size()));return true;}/*** 功能：随机生成一套试卷* * @return*/public void getRandomTest() {BaseWriting baseWriting = (BaseWriting) getRandomObject(BaseWriting.class, "BaseWriting");setBaseWriting(baseWriting);genes[0] = baseWriting.getId();BaseListening baseListening = (BaseListening) getRandomObject(BaseListening.class, "BaseListening");setBaseListening(baseListening);genes[1] = baseListening.getId();BaseWordunderstand baseWordunderstand = (BaseWordunderstand) getRandomObject(BaseWordunderstand.class, "BaseWordunderstand");setBaseWordunderstand(baseWordunderstand);genes[2] = baseWordunderstand.getId();BaseLongreading baseLongreading = (BaseLongreading) getRandomObject(BaseLongreading.class, "BaseLongreading");setBaseLongreading(baseLongreading);genes[3] = baseLongreading.getId();BaseCarereading baseCarereading1 = (BaseCarereading) getRandomObject(BaseCarereading.class, "BaseCarereading");setBaseCarereading1(baseCarereading1);genes[4] = baseCarereading1.getId();BaseCarereading baseCarereading2;do {baseCarereading2 = (BaseCarereading) getRandomObject(BaseCarereading.class, "BaseCarereading");} while (baseCarereading2.getId() == baseCarereading1.getId());setBaseCarereading2(baseCarereading2);genes[5] = baseCarereading2.getId();BaseTranslate baseTranslate = (BaseTranslate) getRandomObject(BaseTranslate.class, "BaseTranslate");setBaseTranslate(baseTranslate);genes[6] = baseTranslate.getId();}/*** 功能：随机获取一个题型对象* * @param clas* @param table* @return*/public Object getRandomObject(Class<?> clas, String table) {ObjectService objectService = Algorithm.objectService;Random random = new Random();Object obj = objectService.getObjectById(clas, random.nextInt(objectService.getMaxID(table)) + 1);return obj;}// Property accessorspublic Integer getId() {return this.id;}public void setId(Integer id) {this.id = id;}public BaseLongreading getBaseLongreading() {return this.baseLongreading;}public void setBaseLongreading(BaseLongreading baseLongreading) {this.baseLongreading = baseLongreading;}public BaseTranslate getBaseTranslate() {return this.baseTranslate;}public void setBaseTranslate(BaseTranslate baseTranslate) {this.baseTranslate = baseTranslate;}public BaseListening getBaseListening() {return this.baseListening;}public void setBaseListening(BaseListening baseListening) {this.baseListening = baseListening;}public BaseCarereading getBaseCarereading1() {return this.baseCarereading1;}public void setBaseCarereading1(BaseCarereading baseCarereading1) {this.baseCarereading1 = baseCarereading1;}public BaseWordunderstand getBaseWordunderstand() {return this.baseWordunderstand;}public void setBaseWordunderstand(BaseWordunderstand baseWordunderstand) {this.baseWordunderstand = baseWordunderstand;}public BaseWriting getBaseWriting() {return this.baseWriting;}public void setBaseWriting(BaseWriting baseWriting) {this.baseWriting = baseWriting;}public BaseCarereading getBaseCarereading2() {return this.baseCarereading2;}public void setBaseCarereading2(BaseCarereading baseCarereading2) {this.baseCarereading2 = baseCarereading2;}public String getName() {return this.name;}public void setName(String name) {this.name = name;}public void setIsmeta(int ismeta) {this.ismeta = ismeta;}public int getIsmeta() {return ismeta;}}

package com.cet.tool;import com.cet.pojo.Test;/*** 功能：遗传算法种群类* * @author alin* */
public class Population {Test[] Tests;// 个体集合// 创建一个种群,初始化时initialise=truepublic Population(int populationSize, boolean initialise) {Tests = new Test[populationSize];// 初始化种群if (initialise) {for (int i = 0; i < size(); i++) {Test newTest = new Test();newTest.getRandomTest();saveTest(i, newTest);}}}public Test getTest(int index) {return Tests[index];}// 获取种群适应度最高的个体public Test getFittest() {Test fittest = Tests[0];for (int i = 0; i < size(); i++) {if (fittest.getFitness(Algorithm.ep) <= getTest(i).getFitness(Algorithm.ep)) {fittest = getTest(i);}}return fittest;}// 获取种群大小public int size() {return Tests.length;}// 保存试卷到种群public void saveTest(int index, Test indiv) {Tests[index] = indiv;}
}

package com.cet.tool;import com.cet.pojo.Test;
import com.cet.service.ObjectService;/*** 功能：遗传算法主类* * @author alin* */
public class AutoGATest {/*** 功能：遗传算法生成试卷主方法* * @param objectService* @param difficulty*            试卷难度* @param ep*            期望难度* @param uniformRate*            交叉概率* @param mutationRate*            变异概率* @param tournamentSize*            淘汰数组大小* @param popSize*            种群大小* @param isCheck*            每次交叉或变异生成的个体是否需要检查重合，考试时检查，练习时不需检查* @return*/public Test getGATest(ObjectService objectService, float ep,double uniformRate, double mutationRate, int tournamentSize,int popSize, boolean isCheck) {// 设置遗传算法相关参数Algorithm.ep = ep;// 设置期望难度Algorithm.uniformRate = uniformRate;// 设置交叉概率Algorithm.mutationRate = mutationRate;// 设置变异概率Algorithm.tournamentSize = tournamentSize;// 设置淘汰数组大小boolean isFirst = true;// 第一次进化，不进行精英主义，因为如果进行，是将初始的种群的精英放入新种群，而不会让它进行任何交叉变异，而有可能它是与数据库某Test重合的Algorithm.objectService = objectService;Population myPop = new Population(popSize, true);// 初始化一个种群// 不断迭代，进行进化操作。 直到找到期望的基因序列,默认都要进化一次，因为这样，进化后的种群个体都是经过交叉变异来的，// 交叉变异过程中，我进行了重复检查，如果个体和数据库的Test数据重合，则重新赋值一个不重复的。这样避免了// 未进化种群就找到了最优个体，而该最优个体与数据库Test数据重合的问题int generationCount = 0;long begin = System.currentTimeMillis();int Count = isCheck == true ? 10 : 100;do {if (myPop.getFittest().getFitness(Algorithm.ep) < 0.90// 种群中最好的个体适应度都低于0.9，则加大交叉概率&& Algorithm.uniformRate < 0.97) {System.out.print(" 加大交叉概率 ");Algorithm.uniformRate += 0.07;}if (myPop.getFittest().getFitness(Algorithm.ep) < 0.90// 种群中最好的个体适应度都低于0.9，则加大变异概率&& Algorithm.mutationRate < 0.25) {System.out.println(" 加大变异概率 ");Algorithm.mutationRate += 0.03;}if (myPop.getFittest().getFitness(Algorithm.ep) > 0.90// 种群中最好的个体适应度高于0.9，则减小交叉概率&& Algorithm.uniformRate > 0.90) {System.out.print(" 减小交叉概率 ");Algorithm.uniformRate -= 0.07;}if (myPop.getFittest().getFitness(Algorithm.ep) > 0.90// 种群中最好的个体适应度高于0.9，则减小变异概率&& Algorithm.mutationRate > 0.15) {System.out.println(" 减小变异概率 ");Algorithm.mutationRate -= 0.03;}myPop = Algorithm.evolvePopulation(myPop, isFirst, isCheck);if (myPop == null) // 题型数量已经不够用了，无法生成新的不重复的试卷return null;isFirst = false;generationCount++;System.out.println("第" + generationCount + "次进化， 适应度为: "+ myPop.getFittest().getFitness(Algorithm.ep) * 100 + "%");} while (myPop.getFittest().getFitness(Algorithm.ep) < 0.98&& generationCount <= Count);System.out.println("用时:" + (System.currentTimeMillis() - begin)/ 1000.0 + "秒");if (generationCount > Count) {System.err.println("题库相关难度的题型不足，无法生成新的试卷\n");return null;}System.out.println("进化完成!");System.out.println("进化总次数:" + generationCount + "，最终个体适应度:"+ myPop.getFittest().getFitness(Algorithm.ep) * 100 + "%"+ "，期望难度:" + ep + ",试卷难度:" + myPop.getFittest().getDifficulty()+ "\n");return myPop.getFittest();}
}