Examples of com.heatonresearch.aifh.evolutionary.train.EvolutionaryAlgorithm

Package com.heatonresearch.aifh.evolutionary.train

Examples of com.heatonresearch.aifh.evolutionary.train.EvolutionaryAlgorithm

com.heatonresearch.aifh.evolutionary.train.EvolutionaryAlgorithm
This interface defines the basic functionality of an Evolutionary Algorithm. An evolutionary algorithm is one that applies operations to a population of potential "solutions".

        Population pop = new BasicPopulation();
        pop.setGenomeFactory(new IntegerArrayGenomeFactory(5));


        // Create a trainer with a very simple score function.  We do not care
        // about the calculation of the score, as they will never be calculated.
        EvolutionaryAlgorithm train = new BasicEA(pop, new ScoreFunction() {
            @Override
            public double calculateScore(MLMethod method) {
                return 0;
            }


            @Override
            public boolean shouldMinimize() {
                return false;
            }
        });


        // Create a shuffle operator.  Use it 1.0 (100%) of the time.
        MutateShuffle opp = new MutateShuffle();
        train.addOperation(1.0, opp);


        // Create a single parent, the genes are set to 1,2,3,4,5.
        IntegerArrayGenome[] parents = new IntegerArrayGenome[1];
        parents[0] = (IntegerArrayGenome) pop.getGenomeFactory().factor();
        for (int i = 1; i <= 5; i++) {

View Full Code Here

        Population pop = new BasicPopulation();
        pop.setGenomeFactory(new DoubleArrayGenomeFactory(5));


        // Create a trainer with a very simple score function.  We do not care
        // about the calculation of the score, as they will never be calculated.
        EvolutionaryAlgorithm train = new BasicEA(pop, new ScoreFunction() {
            @Override
            public double calculateScore(MLMethod method) {
                return 0;
            }


            @Override
            public boolean shouldMinimize() {
                return false;
            }
        });




        MutatePerturb opp = new MutatePerturb(0.1);
        train.addOperation(1.0, opp);




        // Create a peterb operator.  Use it 1.0 (100%) of the time.
        DoubleArrayGenome[] parents = new DoubleArrayGenome[1];
        parents[0] = (DoubleArrayGenome) pop.getGenomeFactory().factor();

View Full Code Here

        GenerateRandom rnd = new MersenneTwisterGenerateRandom();
        EvaluateExpression eval = new EvaluateExpression(rnd);
        Population pop = initPopulation(rnd, eval);
        ScoreFunction score = new ScoreSmallExpression(training,30);


        EvolutionaryAlgorithm genetic = new BasicEA(pop, score);
        genetic.addOperation(0.3, new MutateTree(3));
        genetic.addOperation(0.7, new CrossoverTree());
        genetic.setShouldIgnoreExceptions(false);




        int sameSolutionCount = 0;
        int iteration = 1;
        double lastSolution = Double.MAX_VALUE;
        StringBuilder builder = new StringBuilder();


        while (sameSolutionCount < MAX_SAME_SOLUTION && iteration<1000) {
            genetic.iteration();


            double thisSolution = genetic.getLastError();


            builder.setLength(0);
            builder.append("Iteration: ");
            builder.append(iteration++);
            builder.append(", Current error = ");
            builder.append(thisSolution);
            builder.append(", Best Solution Length = ");
            builder.append(genetic.getBestGenome().size());


            System.out.println(builder.toString());


            if (Math.abs(lastSolution - thisSolution) < 1.0) {
                sameSolutionCount++;
            } else {
                sameSolutionCount = 0;
            }


            lastSolution = thisSolution;
        }


        System.out.println("Good solution found:");
        TreeGenome best = (TreeGenome) genetic.getBestGenome();
        System.out.println(eval.displayExpressionNormal(best.getRoot()));
        genetic.finishTraining();
    }

View Full Code Here

        Population pop = new BasicPopulation();
        pop.setGenomeFactory(new IntegerArrayGenomeFactory(10));


        // Create a trainer with a very simple score function.  We do not care
        // about the calculation of the score, as they will never be calculated.
        EvolutionaryAlgorithm train = new BasicEA(pop, new ScoreFunction() {
            @Override
            public double calculateScore(MLMethod method) {
                return 0;
            }


            @Override
            public boolean shouldMinimize() {
                return false;
            }
        });


        // Create a splice operator, length = 5.  Use it 1.0 (100%) of the time.
        Splice opp = new Splice(5);
        train.addOperation(1.0, opp);


        // Create two parents, the genes are set to 1,2,3,4,5,7,8,9,10
        // and 10,9,8,7,6,5,4,3,2,1.
        IntegerArrayGenome[] parents = new IntegerArrayGenome[2];
        parents[0] = (IntegerArrayGenome) pop.getGenomeFactory().factor();

View Full Code Here

        Population pop = new BasicPopulation();
        pop.setGenomeFactory(new IntegerArrayGenomeFactory(10));


        // Create a trainer with a very simple score function.  We do not care
        // about the calculation of the score, as they will never be calculated.
        EvolutionaryAlgorithm train = new BasicEA(pop, new ScoreFunction() {
            @Override
            public double calculateScore(MLMethod method) {
                return 0;
            }


            @Override
            public boolean shouldMinimize() {
                return false;
            }
        });


        // Create a splice (no repeat) operator, length = 5.  Use it 1.0 (100%) of the time.
        SpliceNoRepeat opp = new SpliceNoRepeat(5);
        train.addOperation(1.0, opp);


        // Create two parents, the genes are set to 1,2,3,4,5,7,8,9,10
        // and 10,9,8,7,6,5,4,3,2,1.
        IntegerArrayGenome[] parents = new IntegerArrayGenome[2];
        parents[0] = (IntegerArrayGenome) pop.getGenomeFactory().factor();

View Full Code Here

        GenerateRandom rnd = new MersenneTwisterGenerateRandom();


        // Create a trainer with a very simple score function.  We do not care
        // about the calculation of the score, as they will never be calculated.
        // We only care that we are maximizing.
        EvolutionaryAlgorithm train = new BasicEA(pop, new ScoreFunction() {
            @Override
            public double calculateScore(MLMethod method) {
                return 0;
            }

View Full Code Here

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Related Classes of com.heatonresearch.aifh.evolutionary.train.EvolutionaryAlgorithm

com.heatonresearch.aifh.examples.gp.FindEquation

com.heatonresearch.aifh.examples.operations.CrossoverExample

com.heatonresearch.aifh.examples.operations.MutateExample

com.heatonresearch.aifh.examples.selection.TournamentCompareExample

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