[R-pkgs] mcga 1.1 (machine coded genetic algorithms) package released

From: Mehmet Hakan Satman <mhsatman_at_yahoo.com>
Date: Fri, 24 Dec 2010 02:12:37 -0800 (PST)


mcga 1.1 (machine coded genetic algorithms) package implements a genetic algorithm optimisation tool with machine coded chromosomes. The machine coded chromosomes stand for chromosomes that are not decoded and encoded. The byte representation of 'double' type variables are crossed-over and mutated. This is different from the binary coded and real coded genetic algorithms. Linux and Windows versions are released but I think it will take a day for the Mac release on cran server.

Suppose that V1 ise a 'double' type C++ variable has a value of 3.141592. This variable has an image of 'sizeof(double)' bytes on the computer memory. And the V2 is an other 'double' type C++ variable with some value. So,

V1 = b[1], b[2], b[3], ..., b[sizeof(double)]

is the byte representation of V1 where b[i] is a 'byte' variable with 256 different values. (This means the alphabet of mcga is length of 256). Suppose that V2 is represented as

V2 = a[1], a[2], a[3], ..., a[sizeof(double)]

where a[i] is same as b[i]. After a crossing-over operation a new offspring, say that, V3 comes into being:

V3 = a[1] or b[1], a[2] or b[2], ...., a[sizeof(double)] or b[sizeof(double)]

where V3 is totally a combination of V1 and V2 and can have values near to V1 and V2 or near to average. This result is similar when compared to real valued chromosomes.

Increasing or decreasing a randomly selected byte of V3 stands for the mutation operator. In mcga, we randomly change a byte like this:


void GA::Mutate(Chromosome *c1){

    char *cptr=(char *)c1->values;
    int mutatepoint=(rand() % (this->chsize * sizeof(double)));     cptr+=mutatepoint;

        if(((float)rand()/RAND_MAX)<0.5){
            (*cptr)+=1;

}else{
(*cptr)-=1;
}

}



Changing a byte of a double value can make a small or extremely high difference. This is the mutation operator of mcga.

 This chromosome coding strategy has some advantages:

  1. This is extremely fast when compared to binary coded ga's when the optimization function is real valued.
  2. Parameters are not bounded by a narrow range. Each single parameter of the goal function can have values between the min(double) and the max(double) on the machine.
  3. A large population can handle a bigger amount of the search space in a faster way, when compared to other encodings.

I know this is like 'genetic programming' but i think mcga only changes the encoding/decoding strategy, it is just a genetic algorithm. More simulations and comparisons should be done for measuring the performance. I am not sure whether this strategy was published anywhere before now. Comments and advices are welcome.

Regards.

Dr.Mehmet Hakan Satman
Istanbul University, Faculty of Economics, Department of Econometrcis http://www.mhsatman.com

      
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