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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