rapid clusters main

    maxGENS=200
    allfits = []
    allrates = []
    parent = ANN()
    parentfit = -9999.0  # We don't know this yet.

    for generation in range(maxGENS) :
        children = []
        mrate = 0.05
        for repc in range(12) :
            gchild  = ANNDuplicate(parent)
            if( (generation==0) and (repc==0) ) :
                gchild = gchild  # Smuggle the parent.
            else :
                gchild.Mutate(mrate)
            children.append(gchild)
            mrate = mrate + 0.01   # use a spectrum of rates.

        time_start = time.time()
        fitpool = FitnessGlobal_Get( children )    # pass the whole list for testing.
        time_end   = time.time()
        elapsed    = time_end - time_start
        if( elapsed > 0.01 ) :
            ratepermin = 60.0 / elapsed
        else :
            ratepermin = 5455.0
        allrates.append(12.0 * ratepermin)

        if( generation == 0 ) :
            parentfit = fitpool[0]

        allfits.append(parentfit)

        # Consider the child with the highest fitness amongst the children.
        childfit = max( fitpool )
        hi = fitpool.index( childfit )
        child = children[hi]

        # print fitpool # debug

        verbosim = "gen={} , {} , {} , {}".format(generation , childfit , parentfit, elapsed)
        print(verbosim)
        if(childfit>parentfit) :
            parentfit=childfit
            parent = ANNDuplicate(child)
            bestWFN = "bestweights_{}.dat".format(generation)
            parent.Send_Synapse_Weights_ToFile(bestWFN)