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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
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0
core-java/0.004102810554955205
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core-java/0.04832801936270381
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core-java/0.5633433244738808
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core-java/0.6256429734439612
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core-java/0.9799201796740292
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48
core-java/src/main/java/com/baeldung/algorithms/RunAlgorithm.java
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@ -8,28 +8,30 @@ import com.baeldung.algorithms.slope_one.SlopeOne;
public class RunAlgorithm { public class RunAlgorithm {
public static void main(String[] args) { public static void main(String[] args) {
Scanner in = new Scanner(System.in); Scanner in = new Scanner(System.in);
System.out.println("Run algorithm:"); System.out.println("Run algorithm:");
System.out.println("1 - Simulated Annealing"); System.out.println("1 - Simulated Annealing");
System.out.println("2 - Slope One"); System.out.println("2 - Slope One");
System.out.println("3 - Simple Genetic Algorithm"); System.out.println("3 - Simple Genetic Algorithm");
int decision = in.nextInt(); int decision = in.nextInt();
switch (decision) { switch (decision) {
case 1: case 1:
System.out.println("Optimized distance for travel: " + SimulatedAnnealing.simulateAnnealing(10, 10000, 0.9995)); System.out.println(
break; "Optimized distance for travel: " + SimulatedAnnealing.simulateAnnealing(10, 10000, 0.9995));
case 2: break;
SlopeOne.slopeOne(3); case 2:
break; SlopeOne.slopeOne(3);
case 3: break;
SimpleGeneticAlgorithm.runAlgorithm(50, "1011000100000100010000100000100111001000000100000100000000001111"); case 3:
break; SimpleGeneticAlgorithm ga = new SimpleGeneticAlgorithm();
default: ga.runAlgorithm(50, "1011000100000100010000100000100111001000000100000100000000001111");
System.out.println("Unknown option"); break;
break; default:
} System.out.println("Unknown option");
in.close(); break;
} }
in.close();
}
} }

20
core-java/src/main/java/com/baeldung/algorithms/ga/binary/SimpleGeneticAlgorithm.java
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@ -11,17 +11,17 @@ public class SimpleGeneticAlgorithm {
private static final boolean elitism = true; private static final boolean elitism = true;
private static byte[] solution = new byte[64]; 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) { if (solution.length() != SimpleGeneticAlgorithm.solution.length) {
throw new RuntimeException("The solution needs to have " + SimpleGeneticAlgorithm.solution.length + " bytes"); throw new RuntimeException("The solution needs to have " + SimpleGeneticAlgorithm.solution.length + " bytes");
} }
SimpleGeneticAlgorithm.setSolution(solution); setSolution(solution);
Population myPop = new Population(populationSize, true); Population myPop = new Population(populationSize, true);
int generationCount = 1; 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()); System.out.println("Generation: " + generationCount + " Correct genes found: " + myPop.getFittest().getFitness());
myPop = SimpleGeneticAlgorithm.evolvePopulation(myPop); myPop = evolvePopulation(myPop);
generationCount++; generationCount++;
} }
System.out.println("Solution found!"); System.out.println("Solution found!");
@ -31,7 +31,7 @@ public class SimpleGeneticAlgorithm {
return true; return true;
} }
public static Population evolvePopulation(Population pop) { public Population evolvePopulation(Population pop) {
int elitismOffset; int elitismOffset;
Population newPopulation = new Population(pop.getIndividuals().size(), false); Population newPopulation = new Population(pop.getIndividuals().size(), false);
@ -56,7 +56,7 @@ public class SimpleGeneticAlgorithm {
return newPopulation; return newPopulation;
} }
private static Individual crossover(Individual indiv1, Individual indiv2) { private Individual crossover(Individual indiv1, Individual indiv2) {
Individual newSol = new Individual(); Individual newSol = new Individual();
for (int i = 0; i < newSol.getDefaultGeneLength(); i++) { for (int i = 0; i < newSol.getDefaultGeneLength(); i++) {
if (Math.random() <= uniformRate) { if (Math.random() <= uniformRate) {
@ -68,7 +68,7 @@ public class SimpleGeneticAlgorithm {
return newSol; return newSol;
} }
private static void mutate(Individual indiv) { private void mutate(Individual indiv) {
for (int i = 0; i < indiv.getDefaultGeneLength(); i++) { for (int i = 0; i < indiv.getDefaultGeneLength(); i++) {
if (Math.random() <= mutationRate) { if (Math.random() <= mutationRate) {
byte gene = (byte) Math.round(Math.random()); 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); Population tournament = new Population(tournamentSize, false);
for (int i = 0; i < tournamentSize; i++) { for (int i = 0; i < tournamentSize; i++) {
int randomId = (int) (Math.random() * pop.getIndividuals().size()); int randomId = (int) (Math.random() * pop.getIndividuals().size());
@ -97,12 +97,12 @@ public class SimpleGeneticAlgorithm {
return fitness; return fitness;
} }
protected static int getMaxFitness() { protected int getMaxFitness() {
int maxFitness = solution.length; int maxFitness = solution.length;
return maxFitness; return maxFitness;
} }
protected static void setSolution(String newSolution) { protected void setSolution(String newSolution) {
solution = new byte[newSolution.length()]; solution = new byte[newSolution.length()];
for (int i = 0; i < newSolution.length(); i++) { for (int i = 0; i < newSolution.length(); i++) {
String character = newSolution.substring(i, i + 1); String character = newSolution.substring(i, i + 1);

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

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spring-hibernate4/.gitignore vendored
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@ -12,3 +12,4 @@
*.war *.war
*.ear *.ear
/target/ /target/
/target/