Examples with SolutionEvaluator evodef.SolutionEvaluator used on opensource projects

Example 1 with SolutionEvaluator

use of evodef.SolutionEvaluator in project SimpleAsteroids by ljialin.

the class EvolveMarioLevelTest method runTrial.

// this should really take any type of EA, but at the moment it
// is restricted to the SimpleRMHC to allow a bespoke mutation operator
// to be plugged in
public double runTrial(SimpleRMHC ea, int nEvals, int[][] sample) {
    int nDims = imageWidth * imageHeight;
    int mValues = distinctValues(sample);
    System.out.println("Distinct values = " + mValues);
    ConvNTuple convNTuple = getTrainedConvNTuple(sample, mValues);
    System.out.println("nSamples: " + convNTuple.nSamples());
    // set the "clever" mutation operator
    ea.setMutator(new ConvMutator().setConvNTuple(convNTuple).setForceBorder(true));
    if (useInitialSeed) {
        ea.setInitialSeed(generateSeed(sample));
    }
    SolutionEvaluator evaluator = new EvalConvNTuple(nDims, mValues).setConvNTuple(convNTuple);
    SolutionEvaluator trainingEvaluator = new EvalConvNTuple(nDims, mValues).setConvNTuple(convNTuple);
    double fitnessFull = trainingEvaluator.evaluate(flatten(sample));
    String labelFull = String.format("Full Width Training Sample: %.6f", fitnessFull);
    LevelView.showMaze(flatten(sample), sample.length, sample[0].length, labelFull, tileColors);
    showSamples(sample, trainingEvaluator);
    evaluator.logger().setListener(this);
    int[] solution = ea.runTrial(evaluator, nEvals);
    // can set entire solution to the most likely individual
    // solution = setAll(solution, 2);
    double fitness = evaluator.evaluate(solution);
    String label = String.format("Fitness: %.6f", fitness);
    // solution = flatten(toRect())
    plotData(evaluator.logger().fa);
    LevelView.showMaze(solution, imageWidth, imageHeight, label, tileColors);
    return fitness;
}

Example 2 with SolutionEvaluator

use of evodef.SolutionEvaluator in project SimpleAsteroids by ljialin.

the class EvolvePatternTest method runTrial.

public double runTrial(SimpleRMHC ea, int nEvals) {
    ConvNTuple convNTuple = getTrainedConvNTuple();
    ea.setMutator(new ConvMutator().setConvNTuple(convNTuple));
    int nDims = imageWidth * imageHeight;
    int mValues = 3;
    SolutionEvaluator evaluator = new EvalConvNTuple(nDims, mValues).setConvNTuple(convNTuple);
    int[] solution = ea.runTrial(evaluator, nEvals);
    double fitness = evaluator.evaluate(solution);
    String label = String.format("Fitness: %.6f", fitness);
    System.out.println(label);
    System.out.println(Arrays.toString(solution));
    LevelView.showMaze(solution, imageWidth, imageHeight, label);
    new JEasyFrame(LineChart.easyPlot(evaluator.logger().fa), "Evolution of Fitness");
    return fitness;
}

Example 3 with SolutionEvaluator

use of evodef.SolutionEvaluator in project SimpleAsteroids by ljialin.

the class EvoNTupleTest method main.

public static void main(String[] args) {
    // the number of bandits is equal to the size of the array
    int nDims = 5;
    int nTrials = 50;
    int nFitnessEvals = 10000;
    EvoAlg ea = new MBanditEA();
    ea = new SimpleRMHC();
    SolutionEvaluator evaluator = new TenFitnessEval(nDims);
    System.out.println("Best fitness stats:");
    System.out.println(runTrials(ea, evaluator, nTrials, nFitnessEvals));
// evaluator.
}