目录 前言 概述 启发式的理解(重点) 优化问题的定义 个体编码 初始族群的创建 评价 配种选择 锦标赛 轮盘赌选择 随机普遍抽样选择 变异 单点交叉 两点交叉 均匀交叉 部分匹配交叉 突变 高斯突变 乱序突变 位翻转突变 均匀整数突变 环境选择 完全重插入(Pure reinsertion) 均匀重插入(Uniform reinsertion) 精英重插入(Elitist reinsertion) 精英保留重插入(Fitness-based reinsertion) 进化算法的python实现

说明:在学习生活中,经常会遇到各种各样的最优问题,其中最常见的就是求某个多维(多个自变量)函数在各个自变量各取何值时的最大值或最小值:例如求函数 f(x) = (x-5)2+(y-6)2+(z-7)2 的最小值,当然,这个函数很简单,很容易看出来,该函数的最小值为0,分别在三个自变量取5,6,7时取得最小值.但日常学习中的函数都是很复杂的,就算通过大量的计算,也不一定能准确地算出目标值以及在何时取得该目标值,因此,本文介绍一种基于单目标的遗传算法来解决此类问题. 注意:由于封装函数较多,为了清晰

NSGA(非支配排序遗传算法).NSGA-II(带精英策略的快速非支配排序遗传算法),都是基于遗传算法的多目标优化算法,是基于pareto最优解讨论的多目标优化. 在官网: http://www.iitk.ac.in/kangal/codes.shtml 可以下载到  NSGA-II  的C语言版源码,下载最新版后打开如下: 其中,nsga2r.c  为主文件,打开后找到核心代码,如下: ; i<=ngen; i++) { selection (parent_pop, child_pop); m

说明:在学习生活中,经常会遇到各种各样的最优问题,其中最常见的就是求某个多维(多个自变量)函数在各个自变量各取何值时的最大值或最小值:例如求函数 f(x) = (x-5)2+(y-6)2+(z-7)2 的最小值,当然,这个函数很简单,很容易看出来,该函数的最小值为0,分别在三个自变量取5,6,7时取得最小值.但日常学习中的函数都是很复杂的,就算通过大量的计算,也不一定能准确地算出目标值以及在何时取得该目标值,因此,本文介绍一种基于单目标的遗传算法来解决此类问题. 注意:由于封装函数较多,为了清晰

一.前言 从学单片机开始鼓捣C语言,到现在为了学CV鼓捣Python,期间在CSDN.简书.博客园和github这些地方得到了很多帮助,所以也想把自己做的一些小东西分享给大家,希望能帮助到别人.记录人生的第一篇博客,mark. 二.图像检测步骤 1. 读取两张图片 第一张是需要检测的小物体,第二章图片是小物体放置在大场景中.代码与输出结果如下所示: import numpy as np import matplotlib.pyplot as plt import cv2 def my_show(

/* Routines for storing population data into files */ # include <stdio.h> # include <stdlib.h> # include <math.h> # include "global.h" # include "rand.h" /* Function to print the information of a population in a file

/* Crowding distance computation routines */ # include <stdio.h> # include <stdlib.h> # include <math.h> # include "global.h" # include "rand.h" /* Routine to compute crowding distance based on ojbective function valu

遗传算法中的交叉操作是 对NSGA-II  源码分析的  最后一部分, 这一部分也是我 从读该算法源代码和看该算法论文理解偏差最大的  函数模块. 这里,首先提一下,遗传算法的  交叉操作.变异操作都是需要设定概率的, 即交叉概率和变异概率. 假设种群个体 大小为  popsize ,  那么交叉操作需要进行 popsize/2 次 ,   变异操作需要进行 popsize 次, 其中每次操作的时候都需要随机生成一个随机数来与给定的概率进行判断,若小于给定的概率则继续执行否则退出该操作. 如果继

遗传算法的变异操作 /* Mutation routines */ # include <stdio.h> # include <stdlib.h> # include <math.h> # include "global.h" # include "rand.h" /* Function to perform mutation in a population */ void mutation_pop (population *p

/* Domination checking routines */ # include <stdio.h> # include <stdlib.h> # include <math.h> # include "global.h" # include "rand.h" /* Routine for usual non-domination checking It will return the following values 1

tourselect.c  文件中共有两个函数: selection (population *old_pop, population *new_pop) individual* tournament (individual *ind1, individual *ind2) 首先,第一个函数代码如下: /* Routine for tournament selection, it creates a new_pop from old_pop by performing tournament se

版权声明:本文为博主原创文章,遵循 CC 4.0 by-sa 版权协议,转载请附上原文出处链接和本声明.本文链接:https://blog.csdn.net/jiang425776024/article/details/84532018 效果图:程序会动态的展示迭代过程,40以内城市大概迭代300次能收敛到最优:这里是用中国城市地理坐标直接做欧式距离计算,实际上可以根据问题作出调整.Github:https://github.com/425776024/TSP-GA-py 测试数据:china.

import os path = r'D:\pywork\12' # 指定位置 if not os.path.exists(path + '/' + '任务集'): #如果目标位置 不存在该文件夹“任务集” ,则执行下面命令 os.mkdir(path + '/' + '任务集') #创建一个新的文件夹 “任务集”,

/* Nond-domination based selection routines */ # include <stdio.h> # include <stdlib.h> # include <math.h> # include "global.h" # include "rand.h" /* Routine to perform non-dominated sorting */ void fill_nondominated_

/* Rank assignment routine */ # include <stdio.h> # include <stdlib.h> # include <math.h> # include "global.h" # include "rand.h" /* Function to assign rank and crowding distance to a population of size pop_size*/ voi

/* Routines for randomized recursive quick-sort */ # include <stdio.h> # include <stdlib.h> # include <math.h> # include "global.h" # include "rand.h" /* Randomized quick sort routine to sort a population based on a p

种群解码函数  decode_pop  为包装函数, 核心调用函数为  decode_ind  , 对每个个体进行解码. /* Routines to decode the population */ # include <stdio.h> # include <stdlib.h> # include <math.h> # include "global.h" # include "rand.h" /* Function to d

This is the Readme file for NSGA-II code. About the Algorithm--------------------------------------------------------------------------NSGA-II: Non-dominated Sorting Genetic Algorithm - II Please refer to the following paper for details about the alg

这部分比较简单,具体的函数数值计算是需要调用设定的目标函数的,此部分一个不能忽略的问题是  超出限制条件的处理 , 故对此加以解释. 首先是包装函数, 核心操作调用  evaluate_ind  实现. /* Routine for evaluating population members */ # include <stdio.h> # include <stdlib.h> # include <math.h> # include "global.h&qu

/* Test problem definitions */ # include <stdio.h> # include <stdlib.h> # include <math.h> # include "global.h" # include "rand.h" # define sch1 /* # define sch2 */ /* # define fon */ /* # define kur */ /* # define po

/* A custom doubly linked list implemenation */ # include <stdio.h> # include <stdlib.h> # include <math.h> # include "global.h" # include "rand.h" /* Insert an element X into the list at location specified by NODE */