iSharkFly-Docs
/
java-tutorials
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Simple Genetic Algorithms improvements (#1146) 

* Modifications to model on Hibernate One to manyTutorial

* Modifications to model on Hibernate One to manyTutorial

* Modifications to model on Hibernate One to manyTutorial

* Simple Genetic Algorithm improvements
This commit is contained in:
maibin 2017-02-10 01:25:40 +01:00 committed by KevinGilmore
parent 30ed03aea1
commit c95097e294
9 changed files with 41 additions and 37 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

0
core-java/0.004102810554955205
View File

0
core-java/0.04832801936270381
View File

0
core-java/0.5633433244738808
View File

0
core-java/0.6256429734439612
View File

0
core-java/0.9799201796740292
View File

48
core-java/src/main/java/com/baeldung/algorithms/RunAlgorithm.java
View File

@ -8,28 +8,30 @@ import com.baeldung.algorithms.slope_one.SlopeOne;
public class RunAlgorithm {
public static void main(String[] args) {
Scanner in = new Scanner(System.in);
System.out.println("Run algorithm:");
System.out.println("1 - Simulated Annealing");
System.out.println("2 - Slope One");
System.out.println("3 - Simple Genetic Algorithm");
int decision = in.nextInt();
switch (decision) {
case 1:
System.out.println("Optimized distance for travel: " + SimulatedAnnealing.simulateAnnealing(10, 10000, 0.9995));
break;
case 2:
SlopeOne.slopeOne(3);
break;
case 3:
SimpleGeneticAlgorithm.runAlgorithm(50, "1011000100000100010000100000100111001000000100000100000000001111");
break;
default:
System.out.println("Unknown option");
break;
}
in.close();
}
public static void main(String[] args) {
Scanner in = new Scanner(System.in);
System.out.println("Run algorithm:");
System.out.println("1 - Simulated Annealing");
System.out.println("2 - Slope One");
System.out.println("3 - Simple Genetic Algorithm");
int decision = in.nextInt();
switch (decision) {
case 1:
System.out.println(
"Optimized distance for travel: " + SimulatedAnnealing.simulateAnnealing(10, 10000, 0.9995));
break;
case 2:
SlopeOne.slopeOne(3);
break;
case 3:
SimpleGeneticAlgorithm ga = new SimpleGeneticAlgorithm();
ga.runAlgorithm(50, "1011000100000100010000100000100111001000000100000100000000001111");
break;
default:
System.out.println("Unknown option");
break;
}
in.close();
}
}

20
core-java/src/main/java/com/baeldung/algorithms/ga/binary/SimpleGeneticAlgorithm.java
View File

@ -11,17 +11,17 @@ public class SimpleGeneticAlgorithm {
private static final boolean elitism = true;
private static byte[] solution = new byte[64];
public static boolean runAlgorithm(int populationSize, String solution) {
public boolean runAlgorithm(int populationSize, String solution) {
if (solution.length() != SimpleGeneticAlgorithm.solution.length) {
throw new RuntimeException("The solution needs to have " + SimpleGeneticAlgorithm.solution.length + " bytes");
}
SimpleGeneticAlgorithm.setSolution(solution);
setSolution(solution);
Population myPop = new Population(populationSize, true);
int generationCount = 1;
while (myPop.getFittest().getFitness() < SimpleGeneticAlgorithm.getMaxFitness()) {
while (myPop.getFittest().getFitness() < getMaxFitness()) {
System.out.println("Generation: " + generationCount + " Correct genes found: " + myPop.getFittest().getFitness());
myPop = SimpleGeneticAlgorithm.evolvePopulation(myPop);
myPop = evolvePopulation(myPop);
generationCount++;
}
System.out.println("Solution found!");
@ -31,7 +31,7 @@ public class SimpleGeneticAlgorithm {
return true;
}
public static Population evolvePopulation(Population pop) {
public Population evolvePopulation(Population pop) {
int elitismOffset;
Population newPopulation = new Population(pop.getIndividuals().size(), false);
@ -56,7 +56,7 @@ public class SimpleGeneticAlgorithm {
return newPopulation;
}
private static Individual crossover(Individual indiv1, Individual indiv2) {
private Individual crossover(Individual indiv1, Individual indiv2) {
Individual newSol = new Individual();
for (int i = 0; i < newSol.getDefaultGeneLength(); i++) {
if (Math.random() <= uniformRate) {
@ -68,7 +68,7 @@ public class SimpleGeneticAlgorithm {
return newSol;
}
private static void mutate(Individual indiv) {
private void mutate(Individual indiv) {
for (int i = 0; i < indiv.getDefaultGeneLength(); i++) {
if (Math.random() <= mutationRate) {
byte gene = (byte) Math.round(Math.random());
@ -77,7 +77,7 @@ public class SimpleGeneticAlgorithm {
}
}
private static Individual tournamentSelection(Population pop) {
private Individual tournamentSelection(Population pop) {
Population tournament = new Population(tournamentSize, false);
for (int i = 0; i < tournamentSize; i++) {
int randomId = (int) (Math.random() * pop.getIndividuals().size());
@ -97,12 +97,12 @@ public class SimpleGeneticAlgorithm {
return fitness;
}
protected static int getMaxFitness() {
protected int getMaxFitness() {
int maxFitness = solution.length;
return maxFitness;
}
protected static void setSolution(String newSolution) {
protected void setSolution(String newSolution) {
solution = new byte[newSolution.length()];
for (int i = 0; i < newSolution.length(); i++) {
String character = newSolution.substring(i, i + 1);

9
core-java/src/test/java/com/baeldung/algorithms/BinaryGeneticAlgorithmUnitTest.java
View File

@ -7,9 +7,10 @@ import com.baeldung.algorithms.ga.binary.SimpleGeneticAlgorithm;
public class BinaryGeneticAlgorithmUnitTest {
@Test
public void testGA() {
Assert.assertTrue(SimpleGeneticAlgorithm.runAlgorithm(50, "1011000100000100010000100000100111001000000100000100000000001111"));
}
@Test
public void testGA() {
SimpleGeneticAlgorithm ga = new SimpleGeneticAlgorithm();
Assert.assertTrue(ga.runAlgorithm(50, "1011000100000100010000100000100111001000000100000100000000001111"));
}
}

1
spring-hibernate4/.gitignore vendored
View File

@ -12,3 +12,4 @@
*.war
*.ear
/target/
/target/