Scrappy Genetic Algorithm! Visualise Styblinski–Tang

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Scrappy Genetic Algorithm! Visualise Styblinski–Tang

Postby hbyte » Tue Feb 09, 2021 7:10 pm

Scrappy Genetic Algorithm! Visualise solving Styblinski–Tang function

This is the Scrappy GA algo. A quick and dirty GA written to be used for Injecting a Deep NN with Wgt and Topology updates.
It has quite a few functions that arnt used in the demo.
There are 2 functions that are viewable on the Glut interface.
I hope this GA is useful not least for its simplicity

1. random genes for each individual in population
2. select proportional to fitness 2 individuals using roullete or rank
3. Breed with crossover and mutate the offspring using probability
4. Replace parents if offspring is fitter
5. Repeat 2-4

Simple. Ive added some elitest features.
We are using 1 point crossover (look it up its the simplest)

[code]void crossover(int job1, int job2){ //Crossover 2 individuals

int point;

for(int l=0;l<len;l++){

if(getlrand(0,1)<cross){

point = rand()%size_;

for(int i=0;i<point;i++){
ind[job2].gene[l][i]=ind[job1].gene[l][i];
}

for(int j=point;j<size_;j++){
ind[job1].gene[l][j]=ind[job2].gene[l][j];
}

}
}
}[/code]


The bin2dec function is a way to convert a binary string into a decimal
number. (Actually its not a string but an array of 0/1's)

There is a good bin2dec on [url=https://www.w3resource.com/c-programming-exercises/for-loop/c-for-loop-exercises-42.php]ww3[/url]
Ive just added some scaling for each problem.

[code]

void bin2dec(data* dt,double lower,double upper){

double dec=0;
double val;
double sum=0;

for(int i=0;i<size;i++){
sum+=pow(2,i);
}

for(int j=0;j<len;j++){

val =1;

for(int i=size;i>0;i--){

if(gene[j][i] == 1){

dec += val;
}
val *=2;

}

var[j]=dt->wgts[j]=(dec/sum)*(upper-lower)+lower;

dec=0;

} }

[/code]

Here is the Scrappy_GA code:

[code]

#include <stdio.h>
#include <stdlib.h>
#include <iostream>
#include <math.h>

using namespace std;

/*Wet InSert GA for RLnet By SD*/
/*Slip in change the dynamic ConMat LUT or slip in and update wgts*/

double getlrand(double lower,double upper){

return ((double) rand()/ RAND_MAX) * (upper-lower) + lower;

}

struct data{

double *wgts;

};

data *dat;


class genome
{
public:
//bit array
double fitness;
int size; //Size of Chromosome
int len; //Number of Chromosomes
int **gene;
double *var;

void init(int size_, int len_)
{

size = size_;
len = len_;
var = new double[len];
gene = new int*[len_];
for(int i=0;i<len_;i++){
gene[i] = new int[size_];
for(int j=0;j<size_;j++){
gene[i][j] = ison(); }}

}

int ison(){

return rand()%2;

}

void bin2dec(data* dt,double lower,double upper){

double dec=0;
double val;
double sum=0;

for(int i=0;i<size;i++){
sum+=pow(2,i);
}

for(int j=0;j<len;j++){

val =1;

for(int i=size;i>0;i--){

if(gene[j][i] == 1){

dec += val;
}
val *=2;

}

var[j]=dt->wgts[j]=(dec/sum)*(upper-lower)+lower;

dec=0;

} }

};

class ga
{

public:
int len; //Length chrom
int size_;
int popsize;
double totalfit,upper_,lower_;
genome *ind; //Array of Individual Bit arrays
double cross,mut; //Operators
int fun,conn,ftest; //Switch forc type


double (*get_err4wgt)(double *wgts); //Return error for net pointer to

func
double (*get_err4con)(int *gene); //Return error for net from conmat

LUT

ga(int popsize_,int size,int chrom_len){
cross=0.8;
mut=0.001;
totalfit=0;
ftest=1; //Testing
conn=1; //Default for conmat
fun=0;
size_ = size;
len = chrom_len;
ind = new genome[popsize_];
popsize = popsize_;

dat = new data;
dat->wgts = new double[len];


for(int i=0;i<popsize;i++)
{
ind[i].init(size_,len);
}
}

void print_ind(int job){

for(int j=0;j<len;j++){
cout<<"Chrom :"<<j<<"\n";
for(int i=0;i<size_;i++){
cout<<ind[job].gene[j][i]<<",";
}
}

}

void print_dat(int job){

ind[job].bin2dec(dat,-5,5); //Internalize range

for(int i=0;i<len;i++){
cout<<dat->wgts[i]<<",";
}

}


double get_fit4func(int job,int funtype){
double sum=0;
double t1,t2,t3,t4;



if(funtype==2){ //Beal function only 2 dim
upper_=4.5;
lower_=-4.5;

ind[job].bin2dec(dat,lower_,upper_);

t1 = pow(1.5-dat->wgts[0]+dat->wgts[0]*dat->wgts[1],2);
t2 = pow(2.25-dat->wgts[0]+dat->wgts[0]*pow(dat->wgts[1],2),2);
t3 = pow(2.625-dat->wgts[0]+dat->wgts[0]*pow(dat->wgts[1],3),2);

return 1/(t1+t2+t3);
}

if(funtype==1){
upper_=5;
lower_=-5;

ind[job].bin2dec(dat,lower_,upper_);

for(int i=0;i<len;i++){ //Each chromo is a variable in solution

to
sum+=pow(dat->wgts[i],4)-16*pow(dat->wgts[i],2)+5*dat->wgts[i];

//Styblinski-Tang
}
return -(sum/2); //Return -39*n
}
if(funtype==0){
upper_=5;
lower_=-5;

ind[job].bin2dec(dat,lower_,upper_);

for(int i=0;i<len;i++){ //DeJongs Sphere (Easy)
sum+= pow(dat->wgts[i],2);
}
return 1/sum; //Return zero
}

//ind[job].fitness = 1/(sum/2);


}

double get_fit4wgts(int job){ //Quickly return fitness of Ind


//ind[job].bin2dec(dat,0,1); //Fill wgts array with doubles

return 1/get_err4wgt(dat->wgts); //Send wgts for net for error =

inv fitness

}

double get_fit4con(int job){


return 1/get_err4con(ind[job].gene[0]); //Use 1 dim gene for

connections

}

void crossover(int job1, int job2){ //Crossover 2 individuals

int point;
//int temp[size_];

for(int l=0;l<len;l++){

if(getlrand(0,1)<cross){

point = rand()%size_;

for(int i=0;i<point;i++){

ind[job2].gene[l][i]=ind[job1].gene[l][i];
}

for(int j=point;j<size_;j++){
ind[job1].gene[l][j]=ind[job2].gene[l][j];
}

}
}
}

void mutate(int job){ //mutate individual uing mut operator


for(int l=0;l<len;l++){

for(int i=0;i<size_;i++){

if(getlrand(0,1)<mut){

if(ind[job].gene[l][i] == 1){

ind[job].gene[l][i] = 0;

}else{

ind[job].gene[l][i] = 1;
}

}
}
}
}

int rank_select(int win){ //Get fittest Clone fitest
double best;
if(win){
best=-10000;}else{
best=10000;
}
int select;

for(int i=0;i<popsize;i++){

if(win){
if(ind[i].fitness>best){
best=ind[i].fitness;
select = i;
}
}else{

if(ind[i].fitness<best){
best=ind[i].fitness;
select = i;
}

}
}
return select;
}


void rank(){
int rnk;
int result[popsize];

for(int i=0;i<popsize;i++){
rnk=0;
for(int j=0;j<popsize;j++){
if(ind[j].fitness<ind[i].fitness)
rnk++;
}
result[i]=rnk;
}

for(int i=0;i<popsize;i++){
cout<<result[i]<<" "<<ind[i].fitness<<"\n";
}

}

void sorted(){ //My sorting algo (blck book!)

genome *tmp;

tmp = new genome[popsize];

for(int i=0;i<popsize;i++)
tmp[i].init(size_,len);

int i,j,sorted,array[popsize],c=0,temp;

for(i=0;i<popsize;i++){
array[i]=i;
}

do{
sorted=1;
for(i=0; i<popsize-1; i++){
if (ind[array[i]].fitness > ind[array[i+1]].fitness){

temp = array[i];
array[i] = array[i+1];
array[i+1] = temp;

sorted=0;
}



}

} while (sorted==0);

for(int i=0;i<popsize;i++)
tmp[i]=ind[i];

for(int i=0;i<popsize;i++)
ind[i]=tmp[array[i]];


}


int roulette_select(){ //Return individual using roulette

selection

double slice = getlrand(0,totalfit);
double fitsofar=0;
int selected=0;

for(int i=0;i<popsize;i++){ //Needs Ranking

selected = i;

fitsofar+= ind[selected].fitness;

if(slice<fitsofar)break;
}



return selected;
}

void replace(int p1,int p2){ //Replace p2 with p1

for(int l=0;l<len;l++){

for(int i=0;i<size_;i++){

ind[p2].gene[l][i] = ind[p1].gene[l][i];
}
}

}

void genecopy(genome ind1,genome ind2){ //Copy genes clone ind1

to ind2

for(int l=0;l<len;l++){
for(int i=0;i<size_;i++){
ind2.gene[l][i] = ind1.gene[l][i];
//ind2.fitness = ind1.fitness;
}
}

}

void getfit(){ //Measure fitness of pop


for(int i=0;i<popsize;i++){

if(ftest==1){
totalfit+= ind[i].fitness = get_fit4func(i,fun);

}else{

if(conn==1){
totalfit+= ind[i].fitness = get_fit4con(i);
}else{
totalfit+= ind[i].fitness = get_fit4wgts(i);
}
}
}



}

void cycle(){

int p1=0,p2=0,p3=0;
totalfit=0;
double fit1,fit2;

genome temp1;
genome temp2;

temp1.init(size_,len);
temp2.init(size_,len);

for(int i=0;i<popsize;i++){

if(ftest==1){
totalfit+= ind[i].fitness = get_fit4func(i,fun);

}else{

if(conn==1){
totalfit+= ind[i].fitness = get_fit4con(i);
}else{
totalfit+= ind[i].fitness = get_fit4wgts(i);
}
}
}



sorted();

if(1){

if(getlrand(0,1)<0.05){
p1 = rank_select(1);
p2 = rank_select(0);
}else{
p1 = roulette_select();
p2 = roulette_select();
}

genecopy(ind[p1],temp1); //Make a backup of parent 1
genecopy(ind[p2],temp2); //Make a backup of parent 2

fit1 = ind[p1].fitness;
fit2 = ind[p2].fitness;

crossover(p1,p2);
mutate(p1);
mutate(p2);

if(getlrand(0,1)<1){
if(get_fit4func(p1,fun)>fit1){ //If children > parents
//Replace parents
p3 = rank_select(0); //Children into pop

}else{
genecopy(temp1,ind[p1]); //Keep parents Kill children


}

if(get_fit4func(p2,fun)>fit2){

p3 = rank_select(0); //Children into pop

}else{

genecopy(temp2,ind[p2]); //Keep parents Kill children Yum

Yum
} }

}

if(0){ //Switch this on if your

feeling elitist!

p1 = rank_select(1); //Find the winner
p2 = rank_select(0); //Find a loser
//cout<<"----"<<p1<<" "<<p2<<"\n";
replace(p1,p2); //Replace loser with winers dna

}



}

};

[/code]

There is a kickass sorting algo that I borrowed from another job:

[code]
void sorted(){ //My sorting algo (blck book!)

genome *tmp;

tmp = new genome[popsize];

for(int i=0;i<popsize;i++)
tmp[i].init(size_,len);

int i,j,sorted,array[popsize],c=0,temp;

for(i=0;i<popsize;i++){
array[i]=i;
}

do{
sorted=1;
for(i=0; i<popsize-1; i++){
if (ind[array[i]].fitness > ind[array[i+1]].fitness){

temp = array[i];
array[i] = array[i+1];
array[i+1] = temp;

sorted=0;
}



}

} while (sorted==0);

for(int i=0;i<popsize;i++)
tmp[i]=ind[i];

for(int i=0;i<popsize;i++)
ind[i]=tmp[array[i]];


}
[/code]

It create an array for the ranking and then uses it to rearrange the

population according to fitness. Soo you dont get in a mess and

roullete needs it.

Here is the Glut interface with main:

[code]
#include <stdlib.h>
#include <stdio.h>
#ifndef WIN32
#include <unistd.h>
#else
#define random rand
#define srandom srand
#endif
#include <math.h>
#include <time.h>
#include <iostream>
#include <GL/glut.h>
#include "Scrappy_GA.cc"

/* Some <math.h> files do not define M_PI... */
#ifndef M_PI
#define M_PI 3.14159265
#endif
#ifndef M_PI_2
#define M_PI_2 1.57079632
#endif

using namespace std;


GLfloat funcpoints[200][200][3];

GLboolean moving = GL_FALSE;

//swarm myswarm(14,3);
ga myga(50,50,2); //50 individuals,12 bin, 300 wgts



double gsize=1; //Problem size
double hgt=1,scl=1,pscl=1; //Position
int res=10.0; //Resolution
int lowres=1;
double scrn = 50; //Screen size
#define MAX_PLANES 14

struct {
float speed; /* zero speed means not flying */
GLfloat red, green, blue;
float theta;
float x, y, z, angle;
} planes[MAX_PLANES];

#define v3f glVertex3f /* v3f was the short IRIS GL name for
glVertex3f */
#define v3d glVertex3d

void loadpoints(void){

double t0,t1,t2,x_,y_,sum=0;

for(int i=0;i<200;i++){

for(int j=0;j<200;j++){

x_ = ((double)i-100)/res;
y_ = ((double)j-100)/res;

if(lowres==1){
x_ = (int)x_;
y_ = (int)y_;
}

double pos[2];

pos[0] = x_;
pos[1] = y_;

if(myga.fun==1){
sum=pow(x_,4)-16*pow(x_,2)+5*x_; //Styblinski-Tang
sum+=pow(y_,4)-16*pow(y_,2)+5*y_;
}
if(myga.fun==0){

sum = pow(x_,2) + pow(y_,2);
}

funcpoints[i][j][0]=x_;
funcpoints[i][j][1]=y_;
funcpoints[i][j][2]=sum/gsize;


}
}

}

void drawlines2buffer(void){
glNewList(1,GL_COMPILE);
glPushMatrix ();

for(int i=0;i<200;i++){
for(int j=0;j<200;j++){
glBegin(GL_LINES);
glColor3f(0.0,1.0,1.0);
if(i<199){
v3d( funcpoints[i][j][0],funcpoints[i][j][1],funcpoints[i][j][2]);
v3d( funcpoints[i+1][j][0],funcpoints[i+1][j][1],funcpoints[i+1]

[j][2]);
}
glEnd();

glBegin(GL_LINES);
glColor3f(0.0,1.0,1.0);
if(j<199){
v3d( funcpoints[i][j][0],funcpoints[i][j][1],funcpoints[i][j][2]);
v3d( funcpoints[i][j+1][0]+0.1,funcpoints[i][j+1][1]

+0.1,funcpoints[i][j+1][2]+0.1);
}
glEnd();

}
}
glPopMatrix ();


glEndList();
}

void
print_bitmap_string(void* font, char* s)
{
if (s && strlen(s)) {
while (*s) {
glutBitmapCharacter(font, *s);
s++;
}
}
}

void
draw(void)
{
GLfloat red, green, blue;
int i,j;


glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);

/*Text output*/
double x = 15.0;
double y = -5.0;
double ystep = 100.0;
double yild = 20.0;
glColor3f(1.0, 1.0, 1.0);
char string[50];

double myfit=0;

if(myga.fun==0){myfit=1/myga.ind[49].fitness;}
if(myga.fun==1){myfit=-myga.ind[49].fitness/2;}

sprintf(string,"Global Best Solution = %f,%f,%f",myfit,myga.ind

[49].var[0],myga.ind[49].var[1]);
int len = strlen(string);
glRasterPos2f(x, y);
print_bitmap_string(GLUT_BITMAP_TIMES_ROMAN_10, string);




/* draw function in 3d */

glCallList(1);


/* paint planes */


glEnable(GL_DEPTH_TEST);
glShadeModel(GL_FLAT);
for (i = 0; i < MAX_PLANES; i++)
if (planes[i].speed != 0.0) {
glPushMatrix();
glTranslatef(planes[i].x, planes[i].y, planes[i].z);
//glScalef(1.0 / 5, 1.0 / 5, 1.0 / 5);
//glRotatef(290.0, 1.0, 0.0, 0.0);
glRotatef(planes[i].angle, 0.0, 0.0, 1.0);
glScalef(1.0 / 12.0, 1.0 / 16.0, 1.0 / 16.0);
glTranslatef(0.0, -4.0, -1.5);

if(1){
glBegin(GL_TRIANGLE_STRIP);
/* left wing */
v3f(-7.0, 0.0, 2.0);
v3f(-1.0, 0.0, 3.0);
glColor3f(red = planes[i].red, green = planes[i].green,
blue = planes[i].blue);
v3f(-1.0, 7.0, 3.0);
/* left side */
glColor3f(0.6 * red, 0.6 * green, 0.6 * blue);
v3f(0.0, 0.0, 0.0);
v3f(0.0, 8.0, 0.0);
/* right side */
v3f(1.0, 0.0, 3.0);
v3f(1.0, 7.0, 3.0);
/* final tip of right wing */
glColor3f(red, green, blue);
v3f(7.0, 0.0, 2.0);
glEnd();
}
//glColor3f(0, 1, 0);
//glutSolidSphere(7.0,50,50);

glPopMatrix();
}
glutSwapBuffers();


}


void reshape(int w, int h)
{
glViewport(0, 0, (GLsizei) w, (GLsizei) h);
glMatrixMode(GL_PROJECTION);
glLoadIdentity();
if (w <= h)
glOrtho(-scrn, scrn, -scrn*(GLfloat)h/(GLfloat)w,
scrn*(GLfloat)h/(GLfloat)w, -scrn, scrn);
else
glOrtho(-scrn*(GLfloat)w/(GLfloat)h,
scrn*(GLfloat)w/(GLfloat)h, -scrn, scrn, -scrn, scrn);

/* Rescale and resize Globally */

glScalef(scl*6.0 / 3.7, scl*6.0 / 3.7, scl*6.0 / 3.7);
glTranslatef(0.0,hgt,0.0);
glRotatef(310,1.0,0.0,0.0);
glRotatef(65,0.0,0.0,1.0);
glRotatef(5,0.0,1.0,0.0);

glMatrixMode(GL_MODELVIEW);
glLoadIdentity();

}

void
tick_per_plane(int i)
{

//myswarm.mainloop(1);
myga.cycle();

float theta = planes[i].theta += planes[i].speed;
planes[i].z = myga.ind[i].fitness/gsize;
planes[i].x = myga.ind[i].var[0];
planes[i].y = myga.ind[i].var[1];
planes[i].angle = ((atan(2.0) + M_PI_2) * sin(theta) - M_PI_2) * 180

/ M_PI;
if (planes[i].speed < 0.0)
planes[i].angle += 180;
}

void
add_plane(void)
{
int i;

for (i = 0; i < MAX_PLANES; i++)
if (planes[i].speed == 0) {

#define SET_COLOR(r,g,b) \
planes[i].red=r; planes[i].green=g; planes[i].blue=b;

switch (random() % 6) {
case 0:
SET_COLOR(1.0, 0.0, 0.0); /* red */
break;
case 1:
SET_COLOR(1.0, 1.0, 1.0); /* white */
break;
case 2:
SET_COLOR(0.0, 1.0, 0.0); /* green */
break;
case 3:
SET_COLOR(1.0, 0.0, 1.0); /* magenta */
break;
case 4:
SET_COLOR(1.0, 1.0, 0.0); /* yellow */
break;
case 5:
SET_COLOR(0.0, 1.0, 1.0); /* cyan */
break;
}
planes[i].speed = ((float) (random() % 20)) * 0.001 + 0.02;
if (random() & 0x1)
planes[i].speed *= -1;
planes[i].theta = ((float) (random() % 257)) * 0.1111;
tick_per_plane(i);
if (!moving)
glutPostRedisplay();
return;
}
}

void
remove_plane(void)
{
int i;

for (i = MAX_PLANES - 1; i >= 0; i--)
if (planes[i].speed != 0) {
planes[i].speed = 0;
if (!moving)
glutPostRedisplay();
return;
}
}

void
tick(void)
{
int i;

for (i = 0; i < MAX_PLANES; i++)
if (planes[i].speed != 0.0)
tick_per_plane(i);
}

void
animate(void)
{
tick();
glutPostRedisplay();
}

void timer(int value){


/* Set the next timed callback */
glutTimerFunc ( 30, timer, 0 ) ;

drawlines2buffer();
glutPostRedisplay () ;
}

void
visible(int state)
{
if (state == GLUT_VISIBLE) {
if (moving)
glutIdleFunc(animate);
} else {
if (moving)
glutIdleFunc(NULL);
}
}

float horiz=0,vert=0,zoom=4.0;


/* ARGSUSED1 */
void
keyboard(unsigned char ch, int x, int y)
{
switch (ch) {
case 'l':
zoom+=1;
glScalef(1.0/zoom,1.0/zoom,1.0/zoom);
glutPostRedisplay();
break;
case 'k':
zoom-=1;
glScalef(1.0/zoom,1.0/zoom,1.0/zoom);
glutPostRedisplay();
break;
case ',':
vert++;
glRotatef(vert,0.0,1.0,0.0);
glutPostRedisplay();
break;
case '.':
vert++;
glRotatef(vert,1.0,0.0,0.0);
glutPostRedisplay();
break;
case '#':
horiz++;
glRotatef(horiz,0.0,1.0,0.0);
glutPostRedisplay();
break;
case '/':
horiz++;
glRotatef(horiz,1.0,0.0,0.0);
glutPostRedisplay();
break;
case ' ':
if (!moving) {
tick();
glutPostRedisplay();
}
break;
case 27: /* ESC */
exit(0);
break;
}
}

#define ADD_PLANE 1
#define REMOVE_PLANE 2
#define MOTION_ON 3
#define MOTION_OFF 4
#define QUIT 5
#define INCREASE_LRT 6

void
menu(int item)
{
switch (item) {
case ADD_PLANE:
add_plane();
break;
case REMOVE_PLANE:
remove_plane();
break;
case MOTION_ON:
moving = GL_TRUE;
glutChangeToMenuEntry(3, "Motion off", MOTION_OFF);
glutIdleFunc(animate);
break;
case MOTION_OFF:
moving = GL_FALSE;
glutChangeToMenuEntry(3, "Motion", MOTION_ON);
glutIdleFunc(NULL);
break;
case INCREASE_LRT:
myga.cross+=0.1;
break;
case QUIT:
exit(0);
break;
}
}

int
main(int argc, char *argv[])
{
hgt = -4.5;scl =3;pscl=1;gsize = 200;
res = 10;lowres=1;scrn=75;

myga.cross=0.7;
myga.mut=0.001;
myga.fun=1; //Which test function

glutInit(&argc, argv);
/* use multisampling if available */
glutInitDisplayMode(GLUT_DOUBLE | GLUT_RGB | GLUT_DEPTH |

GLUT_MULTISAMPLE);
glutCreateWindow("glutplane");
loadpoints();
drawlines2buffer();
glutDisplayFunc(draw);
glutReshapeFunc(reshape);
//glutTimerFunc(30, timer, 0 );
glutKeyboardFunc(keyboard);
//glutVisibilityFunc(visible);
glutCreateMenu(menu);
glutAddMenuEntry("Add plane", ADD_PLANE);
glutAddMenuEntry("Remove plane", REMOVE_PLANE);
glutAddMenuEntry("Motion", MOTION_ON);
glutAddMenuEntry("Inc Lrt", INCREASE_LRT);
glutAddMenuEntry("Quit", QUIT);
glutAttachMenu(GLUT_RIGHT_BUTTON);
/* setup OpenGL state */
glClearDepth(1.0);
glClearColor(0.0, 0.0, 0.0, 0.0);
glMatrixMode(GL_PROJECTION);
glFrustum(-1.0, 1.0, -1.0, 1.0, 1.0, 20);
glMatrixMode(GL_MODELVIEW);
/* add three initial random planes */
srandom(time(NULL));
for(int i=0;i<14;i++){
add_plane(); }

/* start event processing */
glutMainLoop();
return 0; /* ANSI C requires main to return int. */
}
[/code]

Compile with :

g++ -o glutsga glutsga.cc -lglu32 -lopengl32 -lfreeglut

(NB you have to fiddle with the gsize variable to rescale the problems
to fit on the screen e.g fun=0 requires gscale=2 , fun=1 gscale=200)
hbyte
Site Admin
 
Posts: 80
Joined: Thu Aug 13, 2020 6:11 pm

Re: (S)crappy Genetic Algorithm! Visualise Styblinski–Tang

Postby hbyte » Tue Feb 09, 2021 7:20 pm

If you want to use without the gLUT interface here is a simple main function for use with Scrappy:

Code: Select all
int main(){

ga myga(50,50,2);   //50 individuals,50 bin, 2 dim

myga.cross=0.7;
myga.mut=0.07;
myga.fun=1;      //Which test function
 
for(int i=0;i<1500;i++){
myga.cycle();      }

myga.getfit();

myga.sorted();

for(int i=0;i<50;i++){

cout<<"Result for "<<i<<" ";

myga.print_dat(i);

if(myga.fun==1)
cout<<-myga.get_fit4func(i,myga.fun)/2<<"\n";

if(myga.fun==0||myga.fun==2)
cout<<1/myga.get_fit4func(i,myga.fun)<<"\n";


         }
}
hbyte
Site Admin
 
Posts: 80
Joined: Thu Aug 13, 2020 6:11 pm

Re: (S)crappy Genetic Algorithm! Visualise Styblinski–Tang

Postby hbyte » Tue Feb 09, 2021 7:34 pm

You have maximize fitness using Scrappy, so to minimize the functions here I am inverting 1/fitness.
For the Tang problem I just use the negative. I am sure there are many ways.

SD
hbyte
Site Admin
 
Posts: 80
Joined: Thu Aug 13, 2020 6:11 pm


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