evoafs_1.java~

Java遗传算法库

        //int windowSize = 10;  InteractionModel interactionModel = new HistoryWindowIM( windowSize, parsIM );
        Monica monica = new Monica( interactionModel, DUMP_POP_EVERY, java.lang.Integer.MAX_VALUE );
        painter.setEvoTask( monica );
        monica.setName( dirName );
        taskQ.add( monica );
        taskQNames.add( dirName );
        taskQDescr.add( descr );
        taskQBestIndID.add( new Integer( bestIndID ) );
        int avgCirSize = ( int ) ( 1.5 * usedEls );
        int tpLen = 1 << experiment.getNumOfInputs();  int effort = tpLen * nrEvals * avgCirSize * INPUT_SAMPLE_SEP * sumBinCo( avgCirSize, faultDepth ) / 1000000;
        taskQEffort.add( new Integer( effort ) );        
        ControlLib.writeGNUPlotScript( dirName, logDir, logFileName, 3, false );
        ControlLib.writeWebGraphDaemon( dirName, logDir, logFileName, 3, false, new File(".").getCanonicalPath() + File.separator );
    }        
    
    protected  int sumBinCo( int n, int maxR )
    {
        int rv = 0;
        for( int r = 1; r < maxR; r++ )
        {
            rv += ESLib.binomialCoefficient( n, r );
        }
        return rv;
    }
        
        
    protected  void addTTAdd1( String dirName ) throws IOException
    {
        final int INPUT_SAMPLE_SEP = 30;    final int SAMPLE_START = 15;

        BooleanFunction functionC = new Add1bitCFun();  BooleanFunction functionQ = new Add1bitQFun();
         FitnessFunction corrFF = new CorrelationFitnessFunction();
         FitnessFunction tSetupFF = new SampleWindowFitnessFunction( corrFF, SAMPLE_START );
        ArbitraryFunctionExperiment expC = new ArbitraryFunctionExperiment ( functionC, tSetupFF );
        ArbitraryFunctionExperiment expQ = new ArbitraryFunctionExperiment ( functionQ, tSetupFF );
        ConfigurableRandomInputExperiment[] exps = { expQ, expC };  ConfigurableRandomInputMultiOutputExperiment experiment = new ConfigurableRandomInputMultiOutputExperiment( exps );
        final int LUT_INPUTS = 2;   final int E_LINES = 1;  int bitsPerVar = 4;
        final int POP_SIZE = 31;   final int NUM_OF_ELITES = 1;
        final int GENOTYPE_MUT = 1;      int DUMP_POP_EVERY = 2000;

        ElementDelayModel delayModel = new CoinDelayModel( );
        CircuitMapping circuitMapping = new LUTAbsoluteMapping( experiment.getNumOfInputs(), experiment.getNumOfOutputs() + E_LINES, bitsPerVar, LUT_INPUTS, delayModel );
        SimulatorFaultyCircuit circuit = new SimulatorFaultyCircuitAsynchronous( circuitMapping );        
        boolean randomResetBeforeEveryEval = true;  SimulatorDeployment deployment = new SimulatorDeployment( circuit, randomResetBeforeEveryEval );

        int nrAddUnits = ( 1 << bitsPerVar ) - experiment.getNumOfInputs(); int lutSize = 1 << LUT_INPUTS;  int blockSize = lutSize + LUT_INPUTS * bitsPerVar;
        int genotypeLength = nrAddUnits * blockSize;
        Genotype[] seeds = new Genotype[ POP_SIZE ]; 
        Genotype seed = new FullOrderGenotype( genotypeLength );
        //FullOrderGenotype seed = new FullOrderGenotype( "011011011011011010010011011001000001011111111110011010000010101001011110011101100101000010010000100110101100001000001101000111101111011011111110001011010000" );
        //FullOrderGenotype seed = new FullOrderGenotype( "RRQNQ1faOcALlfVu][8D7uRuBG", genotypeLength, 6 );
        seeds[ 0 ] = seed;
        for( int pl = 1; pl < POP_SIZE; pl++ )
        {
            seeds[ pl ] = ( FullOrderGenotype ) seed.clone();
            for( int bl = 0; bl < seeds[ pl ].length(); bl++ )  if( Math.random() < 0.5 )   seeds[ pl ].set( bl );
        }
        int howManyBunches = 1; int qDefSize = 0;   int fixedAlignments = 0;
        ExactGenotypeMutator m = new ExactGenotypeMutator( GENOTYPE_MUT );
        GeneticOperator spxo = new SinglePointXOver();
        GeneticOperator bmin0 = new BunchMutator( bitsPerVar, howManyBunches, blockSize, lutSize, fixedAlignments, qDefSize );
        GeneticOperator bmin1 = new BunchMutator( bitsPerVar, howManyBunches, blockSize, lutSize + bitsPerVar, fixedAlignments, qDefSize );
        //GeneticOperator bmin2 = new BunchMutator( bitsPerVar, howManyBunches, blockSize, lutSize + 2 * bitsPerVar, fixedAlignments, qDefSize );
        //GeneticOperator bmin3 = new BunchMutator( bitsPerVar, howManyBunches, blockSize, lutSize + 3 * bitsPerVar, fixedAlignments, qDefSize );
        GeneticOperator bc = new BlockCopy ( blockSize, blockSize, 0, fixedAlignments, qDefSize );
        //GeneticOperator[] geneticOps ={ m,      spxo,   bmin0,  bmin1,  bmin2,  bmin3,  bc      };
        //double[] opsProbs =          {  0.3,    0.2,    0.1,    0.1  ,  0.1  ,  0.1  ,  0.1     };
        GeneticOperator[] geneticOps ={ m,      spxo,   bmin0,  bmin1,  bc      };
        double[] opsProbs =          {  0.3,    0.2,    0.2,    0.2  ,  0.1     };
        Selector selector = new RankSelector(  );
        Evolver evolver = new StandardEvolver( POP_SIZE, genotypeLength, geneticOps, opsProbs, selector, NUM_OF_ELITES, seeds );
        PopulationLogReader.fullOrderGenotypes = true;
        int nrEvals = 1;    int eSize = 3;  int startAt = 3;
        CircuitPainterObject painter = new CircuitPainterObject( new CircuitPainter(), new LUTAbsoluteMapping( experiment.getNumOfInputs(), experiment.getNumOfOutputs() + E_LINES, bitsPerVar, LUT_INPUTS, new ConstantDelayModel( 0 ) ) ); 
        double[] thresholds = { 0.1, 0.1, 0.1 };    int avgRound = 1;   int faultDepth = 4;
        boolean simpleMode = false;  int getEMode = BISTLib.E_MODE_OSCILLATING;
        TestingTesterBISTPIMComb inIm = new TestingTesterBISTPIMComb( evolver, deployment, experiment, circuit, thresholds, eSize, E_LINES, avgRound, faultDepth, startAt, getEMode, INPUT_SAMPLE_SEP, simpleMode, painter );
        int[] numProps = { 2 }; // Warning, if raise nrEvals will give incorrect value for p0d
        InteractionModel noisyIM = new NoisyPIM( inIm, deployment, experiment, numProps, nrEvals );
        double maxSize = ( 1 << bitsPerVar ) - experiment.getNumOfInputs();
        InteractionModel parsIM = new CircuitParsimonyPIM( noisyIM, circuit, maxSize );
        //int windowSize = 10;
        //InteractionModel interactionModel = new HistoryWindowIM( windowSize, parsIM );
        InteractionModel interactionModel = parsIM;
        Monica monica = new Monica( interactionModel, DUMP_POP_EVERY, java.lang.Integer.MAX_VALUE );
        painter.setEvoTask( monica );
        monica.setName( dirName );  taskQ.add( monica );    taskQNames.add( dirName );

        taskQDescr.add( "Full adder (5 gates) under multiple faults" );
        taskQBestIndID.add( new Integer( 1574 ) );
        int avgTPLen = 8;   int usedEls = 7;
        int effort = 3;
        taskQEffort.add( new Integer( effort ) );
            ControlLib.writeGNUPlotScript( dirName, logDir, logFileName, 3, false );
            ControlLib.writeWebGraphDaemon( dirName, logDir, logFileName, 3, false, new File(".").getCanonicalPath() + File.separator );            
    }
   
    /** Called by clients the first time they connect.
     * @param initialParameters Initial parameters from client local configuration.
     * The current client implementation, see
     * InteractiveTaskClient, provides the ip address of
     * the client and the command line paramaters used to
     * launch the client.  These are packaged in a Vector.
     * @return The ID of this client will from now on used for
     * interaction with the server.
     */
    public Object getID(Object initialParameters) throws RemoteException {
        return ies.getID( initialParameters );
    }
    
    /** This will provide the client with the InteractiveTask
     * it should run.
     * @param id The ID provided by the getID method by which the
     * client will always refer to itself.
     * @return The task this client should run.  This could be
     * exactly the same as the one all other clients are
     * running or tailored to this particular client.
     */
    public InteractiveTask getTask(Object id) throws RemoteException {
        return ies.getTask( id );
    }
    
    /** Called by clients when they wish to interact.
     * @param ID the client's ID
     * @param clientTaskOutput the outputs of the client obtained by task.get( null ) on the client task
     * @return whatever should be sent into the client through task.set( )
     */
    public Object interact(Object ID, Object clientTaskOutput) throws RemoteException {
        return ies.interact( ID, clientTaskOutput );
    }    
    
    public String toString()
    {
        String rv = "EvoBIST with IES: " + ies;
        return rv;
    }
    
    
}