遗传算法与Java代码简单实现

参阅地址：

https://www.jianshu.com/p/ae5157c26af9

代码实现：

public class GA {
 
	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 < 1000000; 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);
 
	}
}