Thursday, May 25, 2017

Open Source: Linear Genetic Programming and Tree Genetic Programming implementation in Java

java-genetic-programming

This package provides java implementation of various genetic programming paradigms such as linear genetic programming, tree genetic programming, gene expression programming, etc
Build Status Coverage Status Documentation Status
More details are provided in the docs for implementation, complexities and further info.

Install

Add the following dependency to your POM file:
<dependency>
  <groupId>com.github.chen0040</groupId>
  <artifactId>java-genetic-programming</artifactId>
  <version>1.0.4</version>
</dependency>

Features

  • Linear Genetic Programming
    • Initialization
      • Full Register Array
      • Fixed-length Register Array
    • Crossover
      • Linear
      • One-Point
      • One-Segment
    • Mutation
      • Micro-Mutation
      • Effective-Macro-Mutation
      • Macro-Mutation
    • Replacement
      • Tournament
      • Direct-Compete
    • Default-Operators
      • Most of the math operators
      • if-less, if-greater
      • Support operator extension
  • Tree Genetic Programming
    • Initialization
      • Full
      • Grow
      • PTC 1
      • Random Branch
      • Ramped Full
      • Ramped Grow
      • Ramped Half-Half
    • Crossover
      • Subtree Bias
      • Subtree No Bias
    • Mutation
      • Subtree
      • Subtree Kinnear
      • Hoist
      • Shrink
    • Evolution Strategy
      • (mu + lambda)
      • TinyGP
Future Works
  • Grammar-based Genetic Programming
  • Gene Expression Programming

Usage of Linear Genetic Programming

Create training data

The sample code below shows how to generate data from the "Mexican Hat" regression problem:
import com.github.chen0040.gp.lgp.gp.BasicObservation;
import com.github.chen0040.gp.lgp.gp.Observation;
import java.util.ArrayList;
import java.util.List;
import java.util.function.BiFunction;

private List<Observation> mexican_hat(){
  List<Observation> result = new ArrayList<>();

  BiFunction<Double, Double, Double> mexican_hat_func = (x1, x2) -> (1 - x1 * x1 / 4 - x2 * x2 / 4) * Math.exp(- x1 * x2 / 8 - x2 * x2 / 8);

  double lower_bound=-4;
  double upper_bound=4;
  int period=16;

  double interval=(upper_bound - lower_bound) / period;

  for(int i=0; i<period; i++)
  {
     double x1=lower_bound + interval * i;
     for(int j=0; j<period; j++)
     {
        double x2=lower_bound + interval * j;

        Observation observation = new BasicObservation(2, 1);

        observation.setInput(0, x1);
        observation.setInput(1, x2);
        observation.setOutput(0, mexican_hat_func.apply(x1, x2));

        result.add(observation);
     }
  }

  return result;
}
We can split the data generated into training and testing data:
import com.github.chen0040.gp.utils.CollectionUtils;

List<Observation> data = mexican_hat();
CollectionUtils.shuffle(data);
TupleTwo<List<Observation>, List<Observation>> split_data = CollectionUtils.split(data, 0.9);
List<Observation> trainingData = split_data._1();
List<Observation> testingData = split_data._2();

Create and train the LGP

The sample code below shows how the LGP can be created and trained:
import com.github.chen0040.gp.lgp.LGP;
import com.github.chen0040.gp.commons.BasicObservation;
import com.github.chen0040.gp.commons.Observation;
import com.github.chen0040.gp.lgp.gp.Population;
import com.github.chen0040.gp.lgp.program.operators.*;

LGP lgp = new LGP();
lgp.getOperatorSet().addAll(new Plus(), new Minus(), new Divide(), new Multiply(), new Power());
lgp.getOperatorSet().addIfLessThanOperator();
lgp.addConstants(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0);
lgp.setRegisterCount(6);
lgp.getObservations().addAll(trainingData);
lgp.setCostEvaluator((program, observations)->{
 double error = 0;
 for(Observation observation : observations){
    program.execute(observation);
    error += Math.pow(observation.getOutput(0) - observation.getPredictedOutput(0), 2.0);
 }

 return error;
});

long startTime = System.currentTimeMillis();
Population pop = lgp.newPopulation();
pop.initialize();
while (!pop.isTerminated())
{
 pop.evolve();
 logger.info("Mexican Hat Symbolic Regression Generation: {}, elapsed: {} seconds", pop.getCurrentGeneration(), (System.currentTimeMillis() - startTime) / 1000);
 logger.info("Global Cost: {}\tCurrent Cost: {}", pop.getGlobalBestProgram().getCost(), pop.getCostInCurrentGeneration());
}

logger.info("best solution found: {}", pop.getGlobalBestProgram());
The last line prints the linear program found by the LGP evolution, a sample of which is shown below:
instruction[1]: <If< r[4] c[0] r[4]>
instruction[2]: <If< r[3] c[3] r[0]>
instruction[3]: <- r[2] r[3] r[2]>
instruction[4]: <* c[7] r[2] r[2]>
instruction[5]: <If< c[2] r[3] r[1]>
instruction[6]: </ r[1] c[4] r[2]>
instruction[7]: <If< r[3] c[7] r[1]>
instruction[8]: <- c[0] r[0] r[0]>
instruction[9]: <If< c[7] r[3] r[4]>
instruction[10]: <- r[2] c[3] r[1]>
instruction[11]: <+ c[4] r[4] r[5]>
instruction[12]: <If< c[2] r[5] r[1]>
instruction[13]: <+ c[7] r[0] r[5]>
instruction[14]: <^ c[7] r[4] r[3]>
instruction[15]: <If< c[3] r[1] r[3]>
instruction[16]: <If< r[1] r[0] r[5]>
instruction[17]: <* c[7] r[2] r[2]>
instruction[18]: <^ r[1] c[6] r[3]>
instruction[19]: <If< r[0] c[5] r[0]>
instruction[20]: <- c[3] r[1] r[3]>
instruction[21]: <If< r[3] c[8] r[0]>
instruction[22]: </ c[2] r[4] r[5]>
instruction[23]: <If< r[3] c[7] r[3]>
instruction[24]: <+ r[0] c[1] r[0]>
instruction[25]: <* r[0] c[6] r[0]>
instruction[26]: <- r[3] c[7] r[1]>
instruction[27]: <- r[4] c[7] r[4]>
instruction[28]: <If< c[1] r[4] r[4]>
instruction[29]: <- c[1] r[0] r[2]>
instruction[30]: </ c[3] r[4] r[3]>
instruction[31]: <If< c[8] r[2] r[2]>
instruction[32]: </ r[1] c[2] r[3]>
instruction[33]: <If< r[0] c[2] r[1]>
instruction[34]: <- c[2] r[2] r[5]>
instruction[35]: <If< c[7] r[5] r[1]>
instruction[36]: <If< r[2] c[5] r[2]>
instruction[37]: <- r[5] c[7] r[3]>
instruction[38]: <- c[8] r[3] r[3]>
instruction[39]: <^ c[3] r[0] r[5]>

Test the program obtained from the LGP evolution

The best program in the LGP population obtained from the training in the above step can then be used for prediction, as shown by the sample code below:
Program program = pop.getGlobalBestProgram();
logger.info("global: {}", program);

for(Observation observation : testingData) {
 program.execute(observation);
 double predicted = observation.getPredictedOutput(0);
 double actual = observation.getOutput(0);

 logger.info("predicted: {}\tactual: {}", predicted, actual);
}

Usage of Tree Genetic Programming

Here we will use the "Mexican Hat" symbolic regression introduced earlier to

Create and train the TreeGP

The sample code below shows how the TreeGP can be created and trained:
import com.github.chen0040.gp.treegp.TreeGP;
import com.github.chen0040.gp.commons.BasicObservation;
import com.github.chen0040.gp.commons.Observation;
import com.github.chen0040.gp.treegp.gp.Population;
import com.github.chen0040.gp.treegp.program.operators.*;

TreeGP tgp = new TreeGP();
tgp.getOperatorSet().addAll(new Plus(), new Minus(), new Divide(), new Multiply(), new Power());
tgp.getOperatorSet().addIfLessThanOperator();
tgp.addConstants(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0);
tgp.setVariableCount(2); // equal to the number of input parameter in an observation
tgp.getObservations().addAll(trainingData);
tgp.setCostEvaluator((program, observations)->{
 double error = 0;
 for(Observation observation : observations){
    program.execute(observation);
    error += Math.pow(observation.getOutput(0) - observation.getPredictedOutput(0), 2.0);
 }

 return error;
});

long startTime = System.currentTimeMillis();
Population pop = tgp.newPopulation();
pop.initialize();
while (!pop.isTerminated())
{
 pop.evolve();
 logger.info("Mexican Hat Symbolic Regression Generation: {}, elapsed: {} seconds", pop.getCurrentGeneration(), (System.currentTimeMillis() - startTime) / 1000);
 logger.info("Global Cost: {}\tCurrent Cost: {}", pop.getGlobalBestProgram().getCost(), pop.getCostInCurrentGeneration());
}

logger.info("best solution found: {}", pop.getGlobalBestSolution().mathExpression());
The last line prints the TreeGP program found by the TreeGP evolution, a sample of which is shown below:
Trees[0]: 1.0 - (if(1.0 < if(1.0 < 1.0, if(1.0 < v0, 1.0, 1.0), if(1.0 < (v1 * v0) + (1.0 / 1.0), 1.0 + 1.0, 1.0)), 1.0, v0 ^ 1.0))

Test the program obtained from the TreeGP evolution

The best program in the TreeGP population obtained from the training in the above step can then be used for prediction, as shown by the sample code below:
Solution program = pop.getGlobalBestSolution();
logger.info("global: {}", program.mathExpression());

for(Observation observation : testingData) {
 program.execute(observation);
 double predicted = observation.getPredictedOutput(0);
 double actual = observation.getOutput(0);

 logger.info("predicted: {}\tactual: {}", predicted, actual);
}
Posted by at
Labels: , , ,

No comments:

Post a Comment

Subscribe to: Post Comments (Atom)