GA.rar_遗传算法

public class GA { Fish fish private int ChrNum = 10; //染色体数量 private String[] ipop = new String[ChrNum]; //一个种群中染色体总数 private int generation = 0; //染色体代号 public static final int GENE = 46; //基因数 private double bestfitness = Double.MAX_VALUE; //函数最优解 private int bestgenerations; //所有子代与父代中最好的染色体 private String beststr; //最优解的染色体的二进制码 /** * 初始化一条染色体（用二进制字符串表示） */ private String initChr() { String res = ""; for (int i = 0; i < GENE; i++) { if (Math.random() > 0.5) { res += "0"; } else { res += "1"; } } return res; } /** * 初始化一个种群(10条染色体) */ private String[] initPop() { String[] ipop = new String[ChrNum]; for (int i = 0; i < ChrNum; i++) { ipop[i] = initChr(); } return ipop; } /** * 将染色体转换成x,y变量的值 */ private double[] calculatefitnessvalue(String str) { //二进制数前23位为x的二进制字符串，后23位为y的二进制字符串 int a = Integer.parseInt(str.substring(0, 23), 2); int b = Integer.parseInt(str.substring(23, 46), 2); double x = a * (6.0 - 0) / (Math.pow(2, 23) - 1); //x的基因 double y = b * (6.0 - 0) / (Math.pow(2, 23) - 1); //y的基因 //需优化的函数 double fitness = 3 - Math.sin(2 * x) * Math.sin(2 * x) - Math.sin(2 * y) * Math.sin(2 * y); double[] returns = { x, y, fitness }; return returns; } /** * 轮盘选择 * 计算群体上每个个体的适应度值; * 按由个体适应度值所决定的某个规则选择将进入下一代的个体; */ private void select() { double evals[] = new double[ChrNum]; // 所有染色体适应值 double p[] = new double[ChrNum]; // 各染色体选择概率 double q[] = new double[ChrNum]; // 累计概率 double F = 0; // 累计适应值总和 for (int i = 0; i < ChrNum; i++) { evals[i] = calculatefitnessvalue(ipop[i])[2]; if (evals[i] < bestfitness){ // 记录下种群中的最小值，即最优解 bestfitness = evals[i]; bestgenerations = generation; beststr = ipop[i]; } F = F + evals[i]; // 所有染色体适应值总和 } for (int i = 0; i < ChrNum; i++) { p[i] = evals[i] / F; if (i == 0) q[i] = p[i]; else { q[i] = q[i - 1] + p[i]; } } for (int i = 0; i < ChrNum; i++) { double r = Math.random(); if (r <= q[0]) { ipop[i] = ipop[0]; } else { for (int j = 1; j < ChrNum; j++) { if (r < q[j]) { ipop[i] = ipop[j]; } } } } } /** * 交叉操作 交叉率为60%，平均为60%的染色体进行交叉 */ private void cross() { String temp1, temp2; for (int i = 0; i < ChrNum; i++) { if (Math.random() < 0.60) { int pos = (int)(Math.random()*GENE)+1; //pos位点前后二进制串交叉 temp1 = ipop[i].substring(0, pos) + ipop[(i + 1) % ChrNum].substring(pos); temp2 = ipop[(i + 1) % ChrNum].substring(0, pos) + ipop[i].substring(pos); ipop[i] = temp1; ipop[(i + 1) / ChrNum] = temp2; } } } /** * 基因突变操作 1%基因变异 */ private void mutation() { for (int i = 0; i < 4; i++) { int num = (int) (Math.random() * GENE * ChrNum + 1); int chromosomeNum = (int) (num / GENE) + 1; // 染色体号 int mutationNum = num - (chromosomeNum - 1) * GENE; // 基因号 if (mutationNum == 0) mutationNum = 1; chromosomeNum = chromosomeNum - 1; if (chromosomeNum >= ChrNum) chromosomeNum = 9; String temp; String a; //记录变异位点变异后的编码 if (ipop[chromosomeNum].charAt(mutationNum - 1) == '0') { //当变异位点为0时 a = "1"; } else { a = "0"; } //当变异位点在首、中段和尾时的突变情况 if (mutationNum == 1) { temp = a + ipop[chromosomeNum].substring(mutationNum); } else { if (mutationNum != GENE) { temp = ipop[chromosomeNum].substring(0, mutationNum -1) + a + ipop[chromosomeNum].substring(mutationNum); } else { temp = ipop[chromosomeNum].substring(0, mutationNum - 1) + a; } } //记录下变异后的染色体 ipop[chromosomeNum] = temp; } } public static void main(String args[]) { GA Tryer = new GA(); Tryer.ipop = Tryer.initPop(); //产生初始种群 String str = ""; //迭代次数 for (int i = 0; i < 100000; i++) { Tryer.select(); Tryer.cross(); Tryer.mutation(); Tryer.generation = i; } double[] x = Tryer.calculatefitnessvalue(Tryer.beststr); str = "最小值" + Tryer.bestfitness + '\n' + "第" + Tryer.bestgenerations + "个染色体:<" + Tryer.beststr + ">" + '\n' + "x=" + x[0] + '\n' + "y=" + x[1]; System.out.println(str); } }