evothreemults.java~

Java遗传算法库

         final int GENOTYPE_MUT = 1;
         final double XOVER_PROB = 0;
         final double MUTATION_PROB = 0.6;
         final int NUM_OF_ELITES = 1;
         final double CHOP_PROB = 0.2;
         final double STRETCH_PROB = 0.2;

         final double TP_XOVER_PROB = 0;
         final double TP_MUTATION_PROB = 1;
         final int TP_NUM_OF_ELITES = 1;

         final double[] RANK_PROBS = { 1d, 0d };
         final double[] TP_RANK_PROBS = { 1d, 0d };
        //
        // D -  Circuit Structure Properties
         final int BITS_PER_VARIABLE = 5;
         final int LUT_INPUTS = 2;

        // D - Simulator Properties
         final int SIMULATOR_GATE_DELAY = 1;
         final double T_SETUP = 0.45;
        // final int INPUT_SAMPLE_SEPARATION = 1;

        // E - Experiment Properties
         final int TEST_LENGTH = 50;

        // M - Log Properties
         int DUMP_POP_EVERY = 1;
        int GENERATIONS = 200000;

        // E - EXPERIMENT set up
         Experiment inExperiment = new MultiplierExperiment( 2, T_SETUP );
        Experiment aonExperiment = new AllOrNothingExperiment( inExperiment );
        
         Experiment tpexp = new TestPattern4EvolvingExperiment( inExperiment );        

        // D - DEPLOYMENT set up
        // SimulatorCircuit circuit = new SimulatorLUTCircuit( SIMULATOR_GATE_DELAY, BITS_PER_VARIABLE , LUT_INPUTS, experiment.getNumOfInputs(), experiment.getNumOfOutputs() );
         ElementDelayModel delayModel = new ConstantDelayModel( SIMULATOR_GATE_DELAY );
         CircuitMapping circuitMapping = new LUTAbsoluteMapping( inExperiment.getNumOfInputs(), inExperiment.getNumOfOutputs(), BITS_PER_VARIABLE, LUT_INPUTS, delayModel );
         SimulatorSimpleCircuit circuit = new SimulatorSimpleCircuit( circuitMapping );
         SimulatorDeployment deployment = new SimulatorDeployment( circuit );

        // E - For Parsimony Experiment
        final int TARGET_GATES = 6;
        Experiment experiment = new ElementParsimonyExperiment( inExperiment, circuit, TARGET_GATES );
        
        // FLEXDeployment deployment = new FLEXDeployment( vFileName,  path2Prog, path2MaxPlus, wrkDir, BITS_PER_VARIABLE, STABILIZERS, GATE_INPUTS, experiment.getNumOfInputs(), experiment.getNumOfOutputs(), PORT_ADD );

        // A - Genetic Operators Set up
         int lutSize = 1 << LUT_INPUTS;
         int blockSize = lutSize + LUT_INPUTS * BITS_PER_VARIABLE;
         int genotypeLength = ( ( 1 << BITS_PER_VARIABLE ) - experiment.getNumOfInputs() ) * blockSize;
         int blockLength = BITS_PER_VARIABLE * LUT_INPUTS + 1 << LUT_INPUTS;
         int testPatLength = ( 1 << experiment.getNumOfInputs() ) * 2;
         int tpGenLength = testPatLength * experiment.getNumOfInputs();

         final Genotype[] SEEDS = { };
         final Genotype[] TP_SEEDS = { };

         //GeneticOperator m = new SAGAMutator( 1, 10 );
         GeneticOperator m = new ExactGenotypeMutator( GENOTYPE_MUT );
        GeneticOperator bmin0 = new BunchMutator( BITS_PER_VARIABLE, 1, blockSize, lutSize );
        GeneticOperator bmin1 = new BunchMutator( BITS_PER_VARIABLE, 1, blockSize, lutSize + BITS_PER_VARIABLE );
        GeneticOperator bmf = new BunchMutator( lutSize, 1, blockSize );
        GeneticOperator bc = new BlockCopy ( blockSize, blockSize );
         GeneticOperator chop = new Chopper( 1 );
         GeneticOperator stretch = new Stretcher( 1, 0 );

         GeneticOperator tpm = new ExactGenotypeMutator( GENOTYPE_MUT );
        GeneticOperator spxo = new TracingSinglePointXOver();
         GeneticOperator tpspxo = new SinglePointXOver();

         // 1+1
         GeneticOperator[] geneticOps = { bmin0, bmin1, bmf, bc };
         GeneticOperator[] tpGeneticOps = { tpm };

         double[] opsProbs = { 0.2,0.3,0.3,0.2 };
         double[] tpOpsProbs = { TP_MUTATION_PROB };
         //
         //
        // Selector selector = new FitnessProportionateSelector();
         Selector selector = new RankSelector( RANK_PROBS );
        //Selector selector = new RankSelector(  );
        // Selector tpSelector = new FitnessProportionateSelector();
        // Selector tpSelector = new RankSelector( TP_RANK_PROBS );
         Selector tpSelector = new RankSelector( TP_RANK_PROBS );

         Evolver evolver = new StandardEvolver( POP_SIZE, genotypeLength, geneticOps, opsProbs, selector, NUM_OF_ELITES, SEEDS );
         Evolver tpEvolver = new StandardEvolver( TP_POP_SIZE, tpGenLength, tpGeneticOps, tpOpsProbs, tpSelector, TP_NUM_OF_ELITES, TP_SEEDS );

         Evolver[] evolvers = { evolver, tpEvolver };
         Experiment[] experiments = { experiment, tpexp };

         InteractionModel interactionModel = new StandardInteractionModel( evolver, deployment, experiment );
         int[] evolutionFrequency = { 1 , 4 };
        // InteractionModel interactionModel = new CircuitTestPatternIM( evolvers, deployment, experiments, evolutionFrequency );

        Monica monica = new Monica( interactionModel, DUMP_POP_EVERY, GENERATIONS );
        String dirName = "2MultParsimony1Plus1-" + id;
        monica.setName( dirName );        
        taskQ.add( monica );
        taskQNames.add( dirName );
        writeGNUPlotScript( dirName, 1 );
    }        
    

    private static void add2MultPostParsimonyP100M50X50( int id, String[] finalPopGenes )
    {

        // A - Genetic Algorithms Properties
        // Standard
         final int POP_SIZE = 103;
         final int TP_POP_SIZE = 7;
         final double BIT_MUTATION_PROB = 0.05;
         final int GENOTYPE_MUT = 1;
         final double XOVER_PROB = 0.45;
         final double ALIEN_PROB = 0;
         final double WIRE_SWAP_PROB = 0.7;
         final double MUTATION_PROB = 0.45;
         final double CHOP_PROB = 0.05;
         final double STRETCH_PROB = 0.05;

         final int NUM_OF_ELITES = 3;

         final double TP_XOVER_PROB = 0.5;
         final double TP_MUTATION_PROB = 0.5;
         final int TP_NUM_OF_ELITES = 2;

        final double[] TP_RANK_PROBS = { 8d, 4d, 2d, 1d };
        //
        // D -  Circuit Structure Properties
         final int BITS_PER_VARIABLE = 5;
         final int LUT_INPUTS = 2;
        // D - Simulator Properties
         final int SIMULATOR_GATE_DELAY = 1;
         final double T_SETUP = 0.45;
        // final int INPUT_SAMPLE_SEPARATION = 1;

        // E - Experiment Properties
         final int TEST_LENGTH = 50;

        // M - Log Properties
         int DUMP_POP_EVERY = 1;
        int GENERATIONS = 5000;

        // E - EXPERIMENT set up
         Experiment inExperiment = new MultiplierExperiment( 2, T_SETUP );
         Experiment tpexp = new TestPattern4EvolvingExperiment( inExperiment );
        Experiment aonExperiment = new AllOrNothingExperiment( inExperiment );

        // D - DEPLOYMENT set up
        // SimulatorCircuit circuit = new SimulatorLUTCircuit( SIMULATOR_GATE_DELAY, BITS_PER_VARIABLE , LUT_INPUTS, experiment.getNumOfInputs(), experiment.getNumOfOutputs() );
         ElementDelayModel delayModel = new ConstantDelayModel( SIMULATOR_GATE_DELAY );
         CircuitMapping circuitMapping = new LUTAbsoluteMapping( inExperiment.getNumOfInputs(), inExperiment.getNumOfOutputs(), BITS_PER_VARIABLE, LUT_INPUTS, delayModel );
         SimulatorSimpleCircuit circuit = new SimulatorSimpleCircuit( circuitMapping );
         SimulatorDeployment deployment = new SimulatorDeployment( circuit );

        // E - For Parsimony Experiment
        final int TARGET_GATES = 6;
        Experiment experiment = new ElementParsimonyExperiment( aonExperiment, circuit, TARGET_GATES );
        
        // FLEXDeployment deployment = new FLEXDeployment( vFileName,  path2Prog, path2MaxPlus, wrkDir, BITS_PER_VARIABLE, STABILIZERS, GATE_INPUTS, experiment.getNumOfInputs(), experiment.getNumOfOutputs(), PORT_ADD );

        // A - Genetic Operators Set up
         int lutSize = 1 << LUT_INPUTS;
         int blockSize = lutSize + LUT_INPUTS * BITS_PER_VARIABLE;
         int genotypeLength = ( ( 1 << BITS_PER_VARIABLE ) - experiment.getNumOfInputs() ) * blockSize;
         int blockLength = BITS_PER_VARIABLE * LUT_INPUTS + 1 << LUT_INPUTS;
         int testPatLength = ( 1 << experiment.getNumOfInputs() ) * 2;
         int tpGenLength = testPatLength * experiment.getNumOfInputs();

         // Doing pool of mud for traceable seeds
        //TraceableGenotype[] SEEDS = new TraceableGenotype[ POP_SIZE ];
        Genotype[] SEEDS = new Genotype[ POP_SIZE ];
        for( int pl = 0; pl < POP_SIZE; pl++ )
        {
            //SEEDS[ pl ] = ( TraceableGenotype ) EvolveLib.randomize( new TraceableGenotype( genotypeLength ) );
            SEEDS[ pl ] = new Genotype( finalPopGenes[ pl ], genotypeLength, 6 );
        }        
        
         final Genotype[] TP_SEEDS = { };

        // BitMutator bm = new BitMutator( BIT_MUTATION_PROB );
        // GeneticOperator m = new ExactGenotypeMutator( GENOTYPE_MUT );
        //GeneticOperator m = new TracingExactGenotypeMutator( GENOTYPE_MUT );
         GeneticOperator m = new ExactGenotypeMutator( GENOTYPE_MUT );
        GeneticOperator bmin0 = new BunchMutator( BITS_PER_VARIABLE, 1, blockSize, lutSize );
        GeneticOperator bmin1 = new BunchMutator( BITS_PER_VARIABLE, 1, blockSize, lutSize + BITS_PER_VARIABLE );
        GeneticOperator bmf = new BunchMutator( lutSize, 1, blockSize );
        GeneticOperator bc = new BlockCopy ( blockSize, blockSize );
         GeneticOperator chop = new Chopper( 1 );
         GeneticOperator stretch = new Stretcher( 1, 0 );

         GeneticOperator tpm = new ExactGenotypeMutator( GENOTYPE_MUT );
        GeneticOperator spxo = new TracingSinglePointXOver();
         GeneticOperator tpspxo = new SinglePointXOver();

         // 1+1
         GeneticOperator[] geneticOps = { bmin0, bmin1, bmf, bc, spxo };
         GeneticOperator[] tpGeneticOps = { tpm };

         double[] opsProbs = { 0.2,0.2,0.2,0.1,0.3 };
         double[] tpOpsProbs = { TP_MUTATION_PROB };
         //
        //
        // Selector selector = new FitnessProportionateSelector();
         //Selector selector = new RankSelector( RANK_PROBS );
        Selector selector = new RankSelector(  );
        // Selector tpSelector = new FitnessProportionateSelector();
        // Selector tpSelector = new RankSelector( TP_RANK_PROBS );
         Selector tpSelector = new RankSelector( TP_RANK_PROBS );

         Evolver evolver = new StandardEvolver( POP_SIZE, genotypeLength, geneticOps, opsProbs, selector, NUM_OF_ELITES, SEEDS );
         Evolver tpEvolver = new StandardEvolver( TP_POP_SIZE, tpGenLength, tpGeneticOps, tpOpsProbs, tpSelector, TP_NUM_OF_ELITES, TP_SEEDS );

         Evolver[] evolvers = { evolver, tpEvolver };
         Experiment[] experiments = { experiment, tpexp };

         InteractionModel interactionModel = new StandardInteractionModel( evolver, deployment, experiment );
         int[] evolutionFrequency = { 1 , 4 };
        // InteractionModel interactionModel = new CircuitTestPatternIM( evolvers, deployment, experiments, evolutionFrequency );

        Monica monica = new Monica( interactionModel, DUMP_POP_EVERY, GENERATIONS );