0/1 knapsack problem Genetic Algorithm

 

# Include <stdio. h>
# Include <stdlib. h>
# Include <time. h>

# Define popsize 200
# Define PC 0.618
# Define PM 0.03.
# Define lchrom 50
# Define maxgen1000

Struct population
{
Int chrom [lchrom];
Double weight;
Double fitness;
} Oldpop [popsize], newpop [popsize];

Int weight [lchrom] = {80, 82, 85, 70, 72, 70, 66, 50, 55, 25,
50, 55, 40, 48, 50, 32, 22, 60, 30, 32,
40, 38, 35, 32, 25, 28, 30, 22, 50, 30,
45, 30, 60, 50, 20, 65, 20, 25, 30, 10,
20, 25, 15, 10, 10, 10, 4, 4, 2, 1 },
Profit [lchrom] ={ 220,208,198,192,180,180,165,162,160,158,
155,130,125,122,120,118,115,110,105,101,
100,100, 98, 96, 95, 90, 88, 82, 80, 77,
75, 73, 72, 70, 69, 66, 65, 63, 60, 58,
56, 50, 30, 20, 15, 10, 8, 5, 3, 1 },
Contain = 1000;

Double sumfitness, maxfitness;
Int maxpop;

Int cal_weight (struct population pop)
{
Int pop_weight = 0 ;;
Int I = 0;

For (I = 0; I <lchrom; I ++)
{
Pop_weight = pop_weight + pop. chrom [I] * weight [I];
}

Return pop_weight;
}

Int cal_fit (struct population pop)
{
Int pop_profit = 0;
Int I = 0;

For (I = 0; I <lchrom; I ++)
{
Pop_profit = pop_profit + pop. chrom [I] * profit [I];
}

Return pop_profit;
}

Void statistics (struct population pop [popsize])
{
Int I = 0;
Sumfitness = pop [0]. fitness;
Maxfitness = pop [0]. fitness;
Maxpop = 0;

For (I = 0; I <popsize; I ++)
{
Sumfitness = sumfitness + pop [I]. fitness;

If (POP [I]. Fitness> maxfitness)
{
Maxfitness = pop [I]. fitness;
Maxpop = I;
}
}
}

Void initpop (void)
{
Int I = 0, j = 0;
Int flag = 0;
Int temp;

For (I = 0; I <popsize; I ++)
{
Flag = 1;
While (FLAG)
{
For (j = 0; j <lchrom; j ++)
{
Oldpop [I]. chrom [J] = rand () % 2;
}

Temp = cal_weight (oldpop [I]);
If (temp <= contain)
{
Oldpop [I]. Weight = temp;
Oldpop [I]. Fitness = cal_fit (oldpop [I]);
Flag = 0;
}
}
}
}

Int execise (double probability)
{
If (RAND () % 20001/20000. 0) <= probability)
Return 1;
 
Return 0;
}

Int selection ()
{
Int I = 0;
Double P = 0.0;
Double rand_number;
 
Rand_number = (RAND () % 2001/2000. 0 );

Do
{
P = P + (oldpop [I]. Fitness/sumfitness );
I ++;
} While (I <popsize) & (P <rand_number ));

Return I-1;
}

Void crossover (INT chr1 [lchrom], int chr2 [lchrom], struct population pop)
{
Int I, cross_pos;

If (execise (PC ))
{
Cross_pos = rand () % (lchrom-1 );
}
Else
Cross_pos = lchrom-1;

For (I = 0; I <= cross_pos; I ++)
{
Pop. chrom [I] = chr1 [I];
}
For (I = cross_pos + 1; I <lchrom; I ++)
{
Pop. chrom [I] = chr2 [I];
}
}

Int mutation (int mut)
{
If (execise (PM ))
{
If (MUT)
Mut = 0;
Else
Mut = 1;
}
Return mut;
}

Void generation ()
{
Int I = 0, j = 0, mate1 = 0, mate2 = 0;
Double temp;
Int flag;

I = 0;
While (I <popsize)
{
Flag = 1;
While (FLAG)
{
Mate1 = selection ();
Mate2 = selection ();

Crossover (oldpop [mate1]. chrom, oldpop [mate2]. chrom, newpop [I]);

For (j = 0; j <lchrom; j ++)
{
Newpop [I]. chrom [J] = mutation (newpop [I]. chrom [J]);
}

Temp = cal_weight (newpop [I]);

If (temp <= contain)
{
Newpop [I]. Weight = temp;
Newpop [I]. Fitness = cal_fit (newpop [I]);
Flag = 0;
}
}
I ++;
}
}

Void print (struct population pop [popsize], int Gen)
{
Int J = 0;
Printf ("the generation is % d. \ n", Gen );
Printf ("the population's Max fitness is % F. \ n", pop [maxpop]. Fitness );
Printf ("the knapsack's weigth is % F. \ n", pop [maxpop]. weight );
Printf ("the population's chrom is :");
For (j = 0; j <lchrom; j ++)
{
If (0 = (J % 5 ))
Printf ("");
Printf ("% d", pop [maxpop]. chrom [J]);
}

Printf ("\ n ");
}

Int main (void)
{
Int I = 0, j = 0, K = 0;
Double oldmax = 0;
Srand (unsigned) Time (null ));
Initpop ();
Statistics (oldpop );
Print (oldpop, 0 );

For (I = 1; I <maxgen; I ++)
{
If (I % 100 = 0)
{
Srand (unsigned) Time (null); // set Random Seed
}

Generation ();
Statistics (newpop );
If (maxfitness <oldmax) continue;
Else
{
Oldmax = maxfitness;
Print (newpop, I );
For (j = 0; j <popsize; j ++)
{
Oldpop [J]. Fitness = newpop [J]. fitness;
Oldpop [J]. Weight = newpop [J]. weight;
For (k = 0; k <lchrom; k ++)
{
Oldpop [J]. chrom [k] = newpop [J]. chrom [k];
}
}
}
}
 
Return 0;
}