Title Page
1a: Technical Paper (html)
1b: Technical Paper (LaTeX)
1c: Technical Paper (pdf)
2: References
3: Sample Runs
4a: CA Code
4b: GA Code
5: Technical Paper Reading
6: Project Description
7: Oral Report
8a: Daily Logs
8b: Bi-Weekly Goals
8c: Final Iteration Progress Report (#6)
9: Scientific Method
10: Tutorial
11: Next Year
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The nature of the Genetic Algorithm is such that output tests, screenshots, and sample runs are not applicable. There is not a way to capture an image of the genetic algorithm.
The Genetic Algorithm is based off of Roulette random choosing, with two-point crossover, one-point crossover, and one-to-one options for breeding. Mutation and elitism are also available.
<![CDATA[
import java.util.Random;
/**
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*
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//Global Variables needed
//Matrix of all rules
//Reassign values after roulette wheel
//Matrix of rules selected by roulette wheel
//What type of breeding:
//One-Point Crossover
//int breeding_choice=1;
//Two-Point Crossover
int breeding_choice=2;
//One-to-one
//int breeding_choice=3;
class rule
{
int[] array = new int[128];
int val;
}
public class GA
{
public static Random rand;
static { rand = new java.util.Random();}
public static void main(rule Rule)
{
int tot=0;
rule[] tempRule = new rule[100];
rule[] tempRule2 = new rule[100];
EvalArray(Rule, tot);
Roulette(Rule, tempRule, tempRule2, tot);
for (int i=0; i<100; i++)
{
Mutation(tempRule2[i]);
}
}
//Evaluate the quality of each rule (already done in CA)
//Evaluate the total quality
//Re-evaluate qualities as a proportion of total
public static void EvalArray(rule Rule, int& tot)
{
for (int i=0; i<128; i++)
{
tot+=Rule[i].val;
}
}
//Roulette Wheel
//Use the evaluated quality to assign portions of the wheel
//Random number generator
//Store chosen rules in matrix
public static void Roulette(rule Rule, rule tempRule, rule tempRule2, int tot)
{
int[] rnd = new int[100];
for (int c=0; c<100; c++)
{
rnd[c]=rand()%tot;
}
for (int j=0; j<100; j++)
{
int temp=0;
int i=0;
do
{
temp+=Rule[i].val;
if (temp > rnd[i] || temp == rnd[i])
{
tempRule[j]=Rule[i];
}
i++;
}while (i<100 && temp<rnd[i]);
}
for (int i=0; i<100; i++)
{
int one=rand()%tot;
int two=rand()%tot;
int var1,var2;
int tempTot=0;
for (int j=0; j<100; j++)
{
tempTot+=Rule[j];
if (tempTot>=one)
{
var1=j;
}
if (tempTot>=two)
{
var2=j;
}
}
Breeding(Rule[var1], Rule[var2], tempRule2, i);
}
}
//Breeding
public static void Breeding(int[] Rule1, int[] Rule2, rule tempRule, n)
{
switch(breeding_choice)
{
case 1:
One_Point_Crossover(Rule1, Rule2, tempRule, n)
break;
case 2:
Two_Point_Crossover(Rule1, Rule2, tempRule, n)
break;
case 3:
One_to_One(Rule1, Rule2, tempRule, n)
break;
}
}
//Crossover
//One-Point
public static void One_Point_Crossover(int[] Rule1, int[] Rule2, rule tempRule, n)
{
int[] t=new array[128];
int point=rand()%128;
for (int i=0; i<128; i++)
{
if (i<point)
{
t[i]=Rule1[i];
}
else
{
t[i]=Rule2[i];
}
}
tempRule[n]=t;
}
//Two-Point
public static void Two_Point_Crossover(int[] Rule1, int[] Rule2, rule tempRule, n)
{
int[] t=new array[128];
int point1=rand()%128;
int point2=rand()%128;
if (point1<point2)
{
for (int i=0; i<128; i++)
{
if (i<point1)
{
t[i]=Rule1[i];
}
else if(i<point2)
{
t[i]=Rule2[i];
}
else
{
t[i]=Rule1[i];
}
}
}
else
{
for (int i=0; i<128; i++)
{
if (i<point2)
{
t[i]=Rule1[i];
}
else if(i<point1)
{
t[i]=Rule2[i];
}
else
{
t[i]=Rule1[i];
}
}
}
tempRule[n]=t;
}
//Random at each spot
public static void One_to_one(int[] Rule1, int[] Rule2, rule tempRule, n)
{
int[] t=new array[128];
for (int i=0; i<128; i++)
{
int rnd=rand()%2;
switch(rnd)
{
case 0:
t[i]=Rule1[i];
break;
case 1:
t[i]=Rule2[i];
break;
}
}
tempRule[n]=t;
}
//Mutation
public static void Mutation(int[] Rule)
{
for (int i=0; i<128; i++)
{
int rnd=rand()%100;
if (rnd == 0)
{
if (Rule[i] == 0)
{
Rule[i]=1;
}
else
{
Rule[i]=0;
}
}
}
}
//Elitism
public static void Elitism(int[] Rule, rule tempRule)
{
tempRule[0] = Rule;
}
}
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