Linear Genetic Programming

Supported Features in Linear Genetic Programming

• Linear Genetic Programming

  • 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

Usage: Create training data

The sample code below shows how to generate data from the “Mexican Hat” regression problem:

import com.github.chen0040.gp.commons.BasicObservation;
import com.github.chen0040.gp.commons.Observation;
import java.util.ArrayList;
import java.util.List;
import java.util.function.BiFunction;

private List<Observation> ProblemCatalogue.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 = ProblemCatalogue.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();

Usage: Create and train the LGP

The sample code below shows how the LGP can be created and trained:

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]>

Usage: 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:

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);
}