📄 geneticalgorithm.txt
字号:
f(x1,x2)=100*(x1^2-x2)^2+(1-x1)^2的
/***************************************************************/
* Simple Genetic Algorithm
* 2002/7/18
*
* 对于整体过程的说明
* main
* GenerateInitialPopulation //产生初始子代，本程序产生0，1 序列
* EvalutePopulation
* |
* |--------CalculateObjectValue //计算目标函数值
* |
* |--------CalculateFitnessValue //计算适应度
* |
* |--------FindBestAndWorstIndividual //计算最好与最差个体
*
* 以下循环到GENERATION代
* GenerateNextPopulation
* |
* |--------SelectOperator //选择操作
* |
* |--------CalculateFitnessValue //单点交叉
* |
* |--------FindBestAndWorstIndividual //变异操作
*
* EvalutePopulation //该函数同上
* .......
* .......
* .......
* PerformEvolution //遗传
* OutputTextReport //输出报告
* 循环结束
****************************************************************
{$N+} //打开8087，因为使用double
program SGA_pascal; //Simple Genetic Algorithm
const
PopSize=160; //Simple Genetic Algorithm
MAXIMIZATION=1; //最大标志
MINIZATION=2; //最小标志
Cmax=100;
Cmin=0;
LENGTH1=10; //第一个变量的遗传变量的长度
LENGTH2=10; //第二个变量的遗传变量的长度
CHROMLENGTH=LENGTH1+LENGTH2; //变量总长度
MaxGeneration=400; //最大遗传代数
Pc=0.6; //交叉概率
Pm=0.001; //变异概率
type
individual =record //个体结构
chrom:array[1..CHROMLENGTH] of char;
value:double;
fitness:double;
end;
var
generation :integer;
best_index :integer; //记录最优秀个体
worst_index :integer; //记录最劣质个体
bestindividual :individual; //当前代最优秀个体
worstindividual :individual; //当前代最差个体
currentbest :individual; //现在得到的最优秀个体
population :array[1..PopSize] of individual;
//种群空间
functionmode :integer; //最优化类型
procedure init; //初始化
procedure generateinitpopulation; //生成最先一代
var i,j:integer;
begin
randomize;
for i:=1 to PopSize do //种群大小
for j:=1 to CHROMLENGTH do //基因长度
if random(10)<5
then population[i].chrom[j]:='0' //以相同概率产生'0','1'
else population[i].chrom[j]:='1';
functionMode:=MAXIMIZATION; //最优化类型
end;
begin
generateinitpopulation;
end; {end of init}
procedure main;
procedure EvaluatePopulation;
function DecodeChromosome(s:string;strstart,strend:integer):longint;
//基因解码过程,对于本程序,是将2进制基因转化成10进制数值
var
decimal :longint;
temp :longint;
i :integer;
begin
decimal:=0;
temp:=1; //以左到右，权值升高
for i:=strstart to strend do
begin
if copy(s,i,1)='1' then decimal:=decimal+temp; //转化到十进制
temp:=temp*2;
end;
decodechromosome:=decimal;
end;
procedure calculateobjectvalue;
//计算目标函数值,本程序目前计算为f(x1,x2)=100*(x1^2-x2)^2+(1-x1)^2
var
i:integer;
temp1,temp2:longint;
x1,x2:double;
begin
for i:=1 to PopSize do
begin
temp1:=DecodeChromosome(population[i].chrom,1,LENGTH1);
//分别对两个基因解码
temp2:=DecodeChromosome(population[i].chrom,LENGTH1+1,LENGTH1+LENGTH2);
x1:=4.096*temp1/1023.0-2.048;
//从这里可以看出,本程序是采用均匀量化
x2:=4.096*temp2/1023.0-2.048;
population[i].value:=100*(x1*x1-x2)*(x1*x1-x2)+(1-x1)*(1-x1);
end;
end;
procedure calculatefitnessvalue;
//计算适应度
var
i :integer;
temp:double;
begin
for i:=1 to PopSize do
begin
if functionmode=MAXIMIZATION
then
if population[i].value+Cmin>0.0
then
temp:=Cmin+population[i].value
else
temp:=0.0
else
if functionmode=MINIZATION
then
if population[i].value<Cmax
then
temp:=Cmax-population[i].value
else
temp:=0.0;
population[i].fitness:=temp;
end;
end;
procedure findbestandworstindividual;
//找到当前种群中最优秀个体
var
i :integer;
sum:double;
begin
sum:=0.0;
bestindividual:=population[1];
worstindividual:=population[1];
for i:=2 to PopSize do
begin
if population[i].fitness>bestindividual.fitness
then
begin
bestindividual:=population[i];
best_index:=i;
end;
if population[i].fitness<worstindividual.fitness
then
begin
worstindividual:=population[i];
worst_index:=i;
end;
sum:=sum+population[i].fitness;
end;
if generation=1
then
currentbest:=bestindividual
else
if bestindividual.fitness>currentbest.fitness
then
currentbest:=bestindividual;
end;
begin
calculateobjectvalue;
calculatefitnessvalue;
findbestandworstindividual;
end;
procedure generatenextpopulation;
procedure selectionoperator;
//通过比例选择产生新的基因
var
i,index:integer;
p,sum:double;
cfitness:array[1..PopSize] of double;
newpopulation:array[1..popsize] of individual;
begin
sum:=0.0;
for i:=1 to PopSize do
sum:=sum+population[i].fitness;
for i:=1 to PopSize do
cfitness[i]:=population[i].fitness/sum;
for i:=2 to PopSize do
cfitness[i]:=cfitness[i-1]+cfitness[i];
for i:=1 to popsize do
begin
p:=random(1000) / 1000.0;
index:=0;
while p>cfitness[index] do
index:=index+1;
newpopulation[i]:=population[index];
end;
for i:=1 to popsize do
population[i]:=newpopulation[i];
end;
procedure crossoveroperator;
//单点交叉
var
i,j:integer;
index:array[1..PopSize] of integer;
point,temp:integer;
p:double;
ch:char;
begin
for i:=1 to PopSize do
index[i]:=i;
for i:=1 to PopSize do
begin
point:=random(PopSize-i+1);
temp:=index[i];
index[i]:=index[point+i];
index[point+i]:=temp;
end;
//随机选择一对个体
for i:=1 to PopSize do
begin
p:=random(1000) / 1000.0;
if p<Pc
then
begin
point:=random(CHROMLENGTH-1)+1;
for j:=point to CHROMLENGTH do
begin
ch:=population[index[i]].chrom[j];
population[index[i]].chrom[j]:=population[index[i+1]].chrom[j];
population[index[i+1]].chrom[j]:=ch;
end;
end;
i:=i+1;
end;
//单点交叉
end;
procedure mutationoperator;
//基因变异
var
i,j:integer;
p:double;
⌨️ 快捷键说明
复制代码
Ctrl + C
搜索代码
Ctrl + F
全屏模式
F11
切换主题
Ctrl + Shift + D
显示快捷键
?
增大字号
Ctrl + =
减小字号
Ctrl + -