cuddGenetic.c File Reference

#include "CUDD/util.h"
#include "CUDD/cuddInt.h"

Go to the source code of this file.

Macros
#define	STOREDD(i, j)   storedd[(i)*(numvars+1)+(j)]

Functions
static int	make_random (DdManager *table, int lower)
static int	sift_up (DdManager *table, int x, int x_low)
static int	build_dd (DdManager *table, int num, int lower, int upper)
static int	largest (void)
static int	rand_int (int a)
static int	array_hash (char *array, int modulus)
static int	array_compare (const char *array1, const char *array2)
static int	find_best (void)
static int	PMX (int maxvar)
static int	roulette (int *p1, int *p2)
int	cuddGa (DdManager *table, int lower, int upper)

Variables
static char rcsid []	DD_UNUSED = "$Id: cuddGenetic.c,v 1.30 2012/02/05 01:07:18 fabio Exp $"
static int	popsize
static int	numvars
static int *	storedd
static st_table *	computed
static int *	repeat
static int	large
static int	result
static int	cross

Macro Definition Documentation

STOREDD

#define STOREDD	(		i,
			j
	)		storedd[(i)*(numvars+1)+(j)]

Definition at line 131 of file cuddGenetic.c.

Function Documentation

array_compare()

static int array_compare ( const char *  array1, const char *  array2  )

static

Function********************************************************************

Synopsis [Comparison function for the computed table.]

Description [Comparison function for the computed table. Returns 0 if the two arrays are equal; 1 otherwise.]

SideEffects [None]

SeeAlso []

Definition at line 705 of file cuddGenetic.c.

{
    int i;
    int *intarray1, *intarray2;

    intarray1 = (int *) array1;
    intarray2 = (int *) array2;

    for (i = 0; i < numvars; i++) {
    if (intarray1[i] != intarray2[i]) return(1);
    }
    return(0);

} /* end of array_compare */

array_hash()

static int array_hash ( char *  array, int  modulus  )

static

Function********************************************************************

Synopsis [Hash function for the computed table.]

Description [Hash function for the computed table. Returns the bucket number.]

SideEffects [None]

SeeAlso []

Definition at line 673 of file cuddGenetic.c.

{
    int val = 0;
    int i;
    int *intarray;

    intarray = (int *) array;

    for (i = 0; i < numvars; i++) {
    val = val * 997 + intarray[i];
    }

    return ((val < 0) ? -val : val) % modulus;

} /* end of array_hash */

build_dd()

static int build_dd ( DdManager *  table, int  num, int  lower, int  upper  )

static

Function********************************************************************

Synopsis [Builds a DD from a given order.]

Description [Builds a DD from a given order. This procedure also sifts the final order and inserts into the array the size in nodes of the result. Returns 1 if successful; 0 otherwise.]

SideEffects [None]

SeeAlso []

Definition at line 552 of file cuddGenetic.c.

{
    int     i,j;        /* loop vars */
    int     position;
    int     index;
    int     limit;      /* how large the DD for this order can grow */
    int     size;

    /* Check the computed table. If the order already exists, it
    ** suffices to copy the size from the existing entry.
    */
    if (computed && st_lookup_int(computed,(char *)&STOREDD(num,0),&index)) {
    STOREDD(num,numvars) = STOREDD(index,numvars);
#ifdef DD_STATS
    (void) fprintf(table->out,"\nCache hit for index %d", index);
#endif
    return(1);
    }

    /* Stop if the DD grows 20 times larges than the reference size. */
    limit = 20 * STOREDD(0,numvars);

    /* Sift up the variables so as to build the desired permutation.
    ** First the variable that has to be on top is sifted to the top.
    ** Then the variable that has to occupy the secon position is sifted
    ** up to the second position, and so on.
    */
    for (j = 0; j < numvars; j++) {
    i = STOREDD(num,j);
    position = table->perm[i];
    result = sift_up(table,position,j+lower);
    if (!result) return(0);
    size = table->keys - table->isolated;
    if (size > limit) break;
    }

    /* Sift the DD just built. */
#ifdef DD_STATS
    (void) fprintf(table->out,"\n");
#endif
    result = cuddSifting(table,lower,upper);
    if (!result) return(0);

    /* Copy order and size to table. */
    for (j = 0; j < numvars; j++) {
    STOREDD(num,j) = table->invperm[lower+j];
    }
    STOREDD(num,numvars) = table->keys - table->isolated; /* size of new DD */
    return(1);

} /* end of build_dd */

cuddGa()

int cuddGa	(	DdManager *	table,
		int	lower,
		int	upper
	)

AutomaticEnd Function********************************************************************

Synopsis [Genetic algorithm for DD reordering.]

Description [Genetic algorithm for DD reordering. The two children of a crossover will be stored in storedd[popsize] and storedd[popsize+1] — the last two slots in the storedd array. (This will make comparisons and replacement easy.) Returns 1 in case of success; 0 otherwise.]

SideEffects [None]

SeeAlso []

Definition at line 188 of file cuddGenetic.c.

{
    int     i,n,m;      /* dummy/loop vars */
    int     index;
#ifdef DD_STATS
    double  average_fitness;
#endif
    int     small;      /* index of smallest DD in population */

    /* Do an initial sifting to produce at least one reasonable individual. */
    if (!cuddSifting(table,lower,upper)) return(0);

    /* Get the initial values. */
    numvars = upper - lower + 1; /* number of variables to be reordered */
    if (table->populationSize == 0) {
    popsize = 3 * numvars;  /* population size is 3 times # of vars */
    if (popsize > 120) {
        popsize = 120;  /* Maximum population size is 120 */
    }
    } else {
    popsize = table->populationSize;  /* user specified value */
    }
    if (popsize < 4) popsize = 4;   /* enforce minimum population size */

    /* Allocate population table. */
    storedd = ALLOC(int,(popsize+2)*(numvars+1));
    if (storedd == NULL) {
    table->errorCode = CUDD_MEMORY_OUT;
    return(0);
    }

    /* Initialize the computed table. This table is made up of two data
    ** structures: A hash table with the key given by the order, which says
    ** if a given order is present in the population; and the repeat
    ** vector, which says how many copies of a given order are stored in
    ** the population table. If there are multiple copies of an order, only
    ** one has a repeat count greater than 1. This copy is the one pointed
    ** by the computed table.
    */
    repeat = ALLOC(int,popsize);
    if (repeat == NULL) {
    table->errorCode = CUDD_MEMORY_OUT;
    FREE(storedd);
    return(0);
    }
    for (i = 0; i < popsize; i++) {
    repeat[i] = 0;
    }
    computed = st_init_table(array_compare,array_hash);
    if (computed == NULL) {
    table->errorCode = CUDD_MEMORY_OUT;
    FREE(storedd);
    FREE(repeat);
    return(0);
    }

    /* Copy the current DD and its size to the population table. */
    for (i = 0; i < numvars; i++) {
    STOREDD(0,i) = table->invperm[i+lower]; /* order of initial DD */
    }
    STOREDD(0,numvars) = table->keys - table->isolated; /* size of initial DD */

    /* Store the initial order in the computed table. */
    if (st_insert(computed,(char *)storedd,(char *) 0) == ST_OUT_OF_MEM) {
    FREE(storedd);
    FREE(repeat);
    st_free_table(computed);
    return(0);
    }
    repeat[0]++;

    /* Insert the reverse order as second element of the population. */
    for (i = 0; i < numvars; i++) {
    STOREDD(1,numvars-1-i) = table->invperm[i+lower]; /* reverse order */
    }

    /* Now create the random orders. make_random fills the population
    ** table with random permutations. The successive loop builds and sifts
    ** the DDs for the reverse order and each random permutation, and stores
    ** the results in the computed table.
    */
    if (!make_random(table,lower)) {
    table->errorCode = CUDD_MEMORY_OUT;
    FREE(storedd);
    FREE(repeat);
    st_free_table(computed);
    return(0);
    }
    for (i = 1; i < popsize; i++) {
    result = build_dd(table,i,lower,upper); /* build and sift order */
    if (!result) {
        FREE(storedd);
        FREE(repeat);
        st_free_table(computed);
        return(0);
    }
    if (st_lookup_int(computed,(char *)&STOREDD(i,0),&index)) {
        repeat[index]++;
    } else {
        if (st_insert(computed,(char *)&STOREDD(i,0),(char *)(long)i) ==
        ST_OUT_OF_MEM) {
        FREE(storedd);
        FREE(repeat);
        st_free_table(computed);
        return(0);
        }
        repeat[i]++;
    }
    }

#if 0
#ifdef DD_STATS
    /* Print the initial population. */
    (void) fprintf(table->out,"Initial population after sifting\n");
    for (m = 0; m < popsize; m++) {
    for (i = 0; i < numvars; i++) {
        (void) fprintf(table->out," %2d",STOREDD(m,i));
    }
    (void) fprintf(table->out," : %3d (%d)\n",
               STOREDD(m,numvars),repeat[m]);
    }
#endif
#endif

    small = find_best();
#ifdef DD_STATS
    average_fitness = find_average_fitness();
    (void) fprintf(table->out,"\nInitial population: best fitness = %d, average fitness %8.3f",STOREDD(small,numvars),average_fitness);
#endif

    /* Decide how many crossovers should be tried. */
    if (table->numberXovers == 0) {
    cross = 3*numvars;
    if (cross > 60) {   /* do a maximum of 50 crossovers */
        cross = 60;
    }
    } else {
    cross = table->numberXovers;      /* use user specified value */
    }
    if (cross >= popsize) {
    cross = popsize;
    }

    /* Perform the crossovers to get the best order. */
    for (m = 0; m < cross; m++) {
    if (!PMX(table->size)) {    /* perform one crossover */
        table->errorCode = CUDD_MEMORY_OUT;
        FREE(storedd);
        FREE(repeat);
        st_free_table(computed);
        return(0);
    }
    /* The offsprings are left in the last two entries of the
    ** population table. These are now considered in turn.
    */
    for (i = popsize; i <= popsize+1; i++) {
        result = build_dd(table,i,lower,upper); /* build and sift child */
        if (!result) {
        FREE(storedd);
        FREE(repeat);
        st_free_table(computed);
        return(0);
        }
        large = largest();  /* find the largest DD in population */

        /* If the new child is smaller than the largest DD in the current
        ** population, enter it into the population in place of the
        ** largest DD.
        */
        if (STOREDD(i,numvars) < STOREDD(large,numvars)) {
        /* Look up the largest DD in the computed table.
        ** Decrease its repetition count. If the repetition count
        ** goes to 0, remove the largest DD from the computed table.
        */
        result = st_lookup_int(computed,(char *)&STOREDD(large,0),
                       &index);
        if (!result) {
            FREE(storedd);
            FREE(repeat);
            st_free_table(computed);
            return(0);
        }
        repeat[index]--;
        if (repeat[index] == 0) {
            int *pointer = &STOREDD(index,0);
            result = st_delete(computed, &pointer, NULL);
            if (!result) {
            FREE(storedd);
            FREE(repeat);
            st_free_table(computed);
            return(0);
            }
        }
        /* Copy the new individual to the entry of the
        ** population table just made available and update the
        ** computed table.
        */
        for (n = 0; n <= numvars; n++) {
            STOREDD(large,n) = STOREDD(i,n);
        }
        if (st_lookup_int(computed,(char *)&STOREDD(large,0),
                  &index)) {
            repeat[index]++;
        } else {
            if (st_insert(computed,(char *)&STOREDD(large,0),
            (char *)(long)large) == ST_OUT_OF_MEM) {
            FREE(storedd);
            FREE(repeat);
            st_free_table(computed);
            return(0);
            }
            repeat[large]++;
        }
        }
    }
    }

    /* Find the smallest DD in the population and build it;
    ** that will be the result.
    */
    small = find_best();

    /* Print stats on the final population. */
#ifdef DD_STATS
    average_fitness = find_average_fitness();
    (void) fprintf(table->out,"\nFinal population: best fitness = %d, average fitness %8.3f",STOREDD(small,numvars),average_fitness);
#endif

    /* Clean up, build the result DD, and return. */
    st_free_table(computed);
    computed = NULL;
    result = build_dd(table,small,lower,upper);
    FREE(storedd);
    FREE(repeat);
    return(result);

} /* end of cuddGa */

find_best()

static int find_best ( void  )

static

Function********************************************************************

Synopsis [Returns the index of the fittest individual.]

Description []

SideEffects [None]

SeeAlso []

Definition at line 735 of file cuddGenetic.c.

{
    int i,small;

    small = 0;
    for (i = 1; i < popsize; i++) {
    if (STOREDD(i,numvars) < STOREDD(small,numvars)) {
        small = i;
    }
    }
    return(small);

} /* end of find_best */

largest()

static int largest ( void  )

static

Function********************************************************************

Synopsis [Finds the largest DD in the population.]

Description [Finds the largest DD in the population. If an order is repeated, it avoids choosing the copy that is in the computed table (it has repeat[i] > 1).]

SideEffects [None]

SeeAlso []

Definition at line 623 of file cuddGenetic.c.

{
    int i;  /* loop var */
    int big;    /* temporary holder to return result */

    big = 0;
    while (repeat[big] > 1) big++;
    for (i = big + 1; i < popsize; i++) {
    if (STOREDD(i,numvars) >= STOREDD(big,numvars) && repeat[i] <= 1) {
        big = i;
    }
    }
    return(big);

} /* end of largest */

make_random()

static int make_random ( DdManager *  table, int  lower  )

static

AutomaticStart

Function********************************************************************

Synopsis [Generates the random sequences for the initial population.]

Description [Generates the random sequences for the initial population. The sequences are permutations of the indices between lower and upper in the current order.]

SideEffects [None]

SeeAlso []

Definition at line 448 of file cuddGenetic.c.

{
    int i,j;        /* loop variables */
    int *used;      /* is a number already in a permutation */
    int next;       /* next random number without repetitions */

    used = ALLOC(int,numvars);
    if (used == NULL) {
    table->errorCode = CUDD_MEMORY_OUT;
    return(0);
    }
#if 0
#ifdef DD_STATS
    (void) fprintf(table->out,"Initial population before sifting\n");
    for (i = 0; i < 2; i++) {
    for (j = 0; j < numvars; j++) {
        (void) fprintf(table->out," %2d",STOREDD(i,j));
    }
    (void) fprintf(table->out,"\n");
    }
#endif
#endif
    for (i = 2; i < popsize; i++) {
        for (j = 0; j < numvars; j++) {
        used[j] = 0;
    }
    /* Generate a permutation of {0...numvars-1} and use it to
    ** permute the variables in the layesr from lower to upper.
    */
        for (j = 0; j < numvars; j++) {
        do {
        next = rand_int(numvars-1);
        } while (used[next] != 0);
        used[next] = 1;
        STOREDD(i,j) = table->invperm[next+lower];
        }
#if 0
#ifdef DD_STATS
    /* Print the order just generated. */
    for (j = 0; j < numvars; j++) {
        (void) fprintf(table->out," %2d",STOREDD(i,j));
    }
    (void) fprintf(table->out,"\n");
#endif
#endif
    }
    FREE(used);
    return(1);

} /* end of make_random */

PMX()

static int PMX ( int  maxvar )

static

Function********************************************************************

Synopsis [Returns the average fitness of the population.]

Description []

SideEffects [None]

SeeAlso [] Function********************************************************************

Synopsis [Performs the crossover between two parents.]

Description [Performs the crossover between two randomly chosen parents, and creates two children, x1 and x2. Uses the Partially Matched Crossover operator.]

SideEffects [None]

SeeAlso []

Definition at line 793 of file cuddGenetic.c.

{
    int     cut1,cut2;  /* the two cut positions (random) */
    int     mom,dad;    /* the two randomly chosen parents */
    int     *inv1;      /* inverse permutations for repair algo */
    int     *inv2;
    int     i;      /* loop vars */
    int     u,v;        /* aux vars */

    inv1 = ALLOC(int,maxvar);
    if (inv1 == NULL) {
    return(0);
    }
    inv2 = ALLOC(int,maxvar);
    if (inv2 == NULL) {
    FREE(inv1);
    return(0);
    }

    /* Choose two orders from the population using roulette wheel. */
    if (!roulette(&mom,&dad)) {
    FREE(inv1);
    FREE(inv2);
    return(0);
    }

    /* Choose two random cut positions. A cut in position i means that
    ** the cut immediately precedes position i.  If cut1 < cut2, we
    ** exchange the middle of the two orderings; otherwise, we
    ** exchange the beginnings and the ends.
    */
    cut1 = rand_int(numvars-1);
    do {
    cut2 = rand_int(numvars-1);
    } while (cut1 == cut2);

#if 0
    /* Print out the parents. */
    (void) fprintf(table->out,
           "Crossover of %d (mom) and %d (dad) between %d and %d\n",
           mom,dad,cut1,cut2);
    for (i = 0; i < numvars; i++) {
    if (i == cut1 || i == cut2) (void) fprintf(table->out,"|");
    (void) fprintf(table->out,"%2d ",STOREDD(mom,i));
    }
    (void) fprintf(table->out,"\n");
    for (i = 0; i < numvars; i++) {
    if (i == cut1 || i == cut2) (void) fprintf(table->out,"|");
    (void) fprintf(table->out,"%2d ",STOREDD(dad,i));
    }
    (void) fprintf(table->out,"\n");
#endif

    /* Initialize the inverse permutations: -1 means yet undetermined. */
    for (i = 0; i < maxvar; i++) {
    inv1[i] = -1;
    inv2[i] = -1;
    }

    /* Copy the portions whithin the cuts. */
    for (i = cut1; i != cut2; i = (i == numvars-1) ? 0 : i+1) {
    STOREDD(popsize,i) = STOREDD(dad,i);
    inv1[STOREDD(popsize,i)] = i;
    STOREDD(popsize+1,i) = STOREDD(mom,i);
    inv2[STOREDD(popsize+1,i)] = i;
    }

    /* Now apply the repair algorithm outside the cuts. */
    for (i = cut2; i != cut1; i = (i == numvars-1 ) ? 0 : i+1) {
    v = i;
    do {
        u = STOREDD(mom,v);
        v = inv1[u];
    } while (v != -1);
    STOREDD(popsize,i) = u;
    inv1[u] = i;
    v = i;
    do {
        u = STOREDD(dad,v);
        v = inv2[u];
    } while (v != -1);
    STOREDD(popsize+1,i) = u;
    inv2[u] = i;
    }

#if 0
    /* Print the results of crossover. */
    for (i = 0; i < numvars; i++) {
    if (i == cut1 || i == cut2) (void) fprintf(table->out,"|");
    (void) fprintf(table->out,"%2d ",STOREDD(popsize,i));
    }
    (void) fprintf(table->out,"\n");
    for (i = 0; i < numvars; i++) {
    if (i == cut1 || i == cut2) (void) fprintf(table->out,"|");
    (void) fprintf(table->out,"%2d ",STOREDD(popsize+1,i));
    }
    (void) fprintf(table->out,"\n");
#endif

    FREE(inv1);
    FREE(inv2);
    return(1);

} /* end of PMX */

rand_int()

static int rand_int ( int  a )

static

Function********************************************************************

Synopsis [Generates a random number between 0 and the integer a.]

Description []

SideEffects [None]

SeeAlso []

Definition at line 652 of file cuddGenetic.c.

{
    return(Cudd_Random() % (a+1));

} /* end of rand_int */

roulette()

static int roulette ( int *  p1, int *  p2  )

static

Function********************************************************************

Synopsis [Selects two parents with the roulette wheel method.]

Description [Selects two distinct parents with the roulette wheel method.]

SideEffects [The indices of the selected parents are returned as side effects.]

SeeAlso []

Definition at line 913 of file cuddGenetic.c.

{
    double *wheel;
    double spin;
    int i;

    wheel = ALLOC(double,popsize);
    if (wheel == NULL) {
    return(0);
    }

    /* The fitness of an individual is the reciprocal of its size. */
    wheel[0] = 1.0 / (double) STOREDD(0,numvars);

    for (i = 1; i < popsize; i++) {
    wheel[i] = wheel[i-1] + 1.0 / (double) STOREDD(i,numvars);
    }

    /* Get a random number between 0 and wheel[popsize-1] (that is,
    ** the sum of all fitness values. 2147483561 is the largest number
    ** returned by Cudd_Random.
    */
    spin = wheel[numvars-1] * (double) Cudd_Random() / 2147483561.0;

    /* Find the lucky element by scanning the wheel. */
    for (i = 0; i < popsize; i++) {
    if (spin <= wheel[i]) break;
    }
    *p1 = i;

    /* Repeat the process for the second parent, making sure it is
    ** distinct from the first.
    */
    do {
    spin = wheel[popsize-1] * (double) Cudd_Random() / 2147483561.0;
    for (i = 0; i < popsize; i++) {
        if (spin <= wheel[i]) break;
    }
    } while (i == *p1);
    *p2 = i;

    FREE(wheel);
    return(1);

} /* end of roulette */

sift_up()

static int sift_up ( DdManager *  table, int  x, int  x_low  )

static

Function********************************************************************

Synopsis [Moves one variable up.]

Description [Takes a variable from position x and sifts it up to position x_low; x_low should be less than x. Returns 1 if successful; 0 otherwise]

SideEffects [None]

SeeAlso []

Definition at line 516 of file cuddGenetic.c.

{
    int        y;
    int        size;

    y = cuddNextLow(table,x);
    while (y >= x_low) {
    size = cuddSwapInPlace(table,y,x);
    if (size == 0) {
        return(0);
    }
    x = y;
    y = cuddNextLow(table,x);
    }
    return(1);

} /* end of sift_up */

Variable Documentation

computed

st_table* computed

static

Definition at line 117 of file cuddGenetic.c.

cross

int cross

static

Definition at line 122 of file cuddGenetic.c.

DD_UNUSED

char rcsid [] DD_UNUSED = "$Id: cuddGenetic.c,v 1.30 2012/02/05 01:07:18 fabio Exp $"

static

CFile***********************************************************************

FileName [cuddGenetic.c]

PackageName [cudd]

Synopsis [Genetic algorithm for variable reordering.]

Description [Internal procedures included in this file:

• cuddGa()

Static procedures included in this module:

• make_random()
• sift_up()
• build_dd()
• largest()
• rand_int()
• array_hash()
• array_compare()
• find_best()
• find_average_fitness()
• PMX()
• roulette()

The genetic algorithm implemented here is as follows. We start with the current DD order. We sift this order and use this as the reference DD. We only keep 1 DD around for the entire process and simply rearrange the order of this DD, storing the various orders and their corresponding DD sizes. We generate more random orders to build an initial population. This initial population is 3 times the number of variables, with a maximum of 120. Each random order is built (from the reference DD) and its size stored. Each random order is also sifted to keep the DD sizes fairly small. Then a crossover is performed between two orders (picked randomly) and the two resulting DDs are built and sifted. For each new order, if its size is smaller than any DD in the population, it is inserted into the population and the DD with the largest number of nodes is thrown out. The crossover process happens up to 50 times, and at this point the DD in the population with the smallest size is chosen as the result. This DD must then be built from the reference DD.]

SeeAlso []

Author [Curt Musfeldt, Alan Shuler, Fabio Somenzi]

Copyright [Copyright (c) 1995-2012, Regents of the University of Colorado

All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.]

Definition at line 103 of file cuddGenetic.c.

large

int large

static

Definition at line 119 of file cuddGenetic.c.

numvars

int numvars

static

Definition at line 107 of file cuddGenetic.c.

popsize

int popsize

static

Definition at line 106 of file cuddGenetic.c.

repeat

int* repeat

static

Definition at line 118 of file cuddGenetic.c.

result

int result

static

Definition at line 121 of file cuddGenetic.c.

storedd

int* storedd

static

Definition at line 116 of file cuddGenetic.c.