Re: " GA Instable

"gpapak" <[EMAIL PROTECTED]> wrote:

> I'm using a simple genetic algorithm to solve a
> maximization problem and I encounter the following
> problem:

> Contiguous runnings of the algoritm "may" be resulted to
> different solutions with big distances between them!!!

> My algorithm uses the basic ge[n]tic operators:

>                         sus[???] selection
>                         one point crossover
>                         mutation
>                         reinsertion without elitism

> In other words there is instability about the performance
> of the algorithm.

> Why this event is happened and due to ??

This is very common in genetic algorithms, which aren't
nearly as easy to implement as they look.

First, there is a problem called "local optimums", which
mainly means if your balance between speed of solution and
amount of mutation or strength of other attempts to keep
a diverse (having lots of essentially different solutions)
population in your GA isn't good, the GA will jump on the
first fairly good solution it finds and never let go; so
increasing mutation might help.  Since different runs, due
to randomness, are likely to grab different first fairly good
solutions, what you are seeing happens.  It is also possible
to have too _much_ mutation, and have it destroy all
attempts to nudge toward a solution.

Second, GAs can take a _lot_ longer to wander to a solution
than you might at first guess; a GA is rarely the way to
solve a problem solvable by some older methods, it is mostly
good for solving problems for which no other feasible
approach is known, so willingness to invest a lot of run
time is crucial.

Third, too small a population might not sample enough of the
problem space to have any chance of finding a good solution,
so population sizes in the hundreds or even thousands may be
needed.

A good start is to use a large initially random population
and *no mutation at all*, then in subsequent runs, to up the
mutation-at-all rate by 0.005 at most per try, and the "how
much mutation per occurance" rate also keep small, until you
find a working combination. Each setting of course should be
used for several tries to see if you are getting convergence
to similar solutions or still having the trouble you describe.

I hope that helps, and I hope even more my rather poor writing
style makes it across the second-language barrier.

xanthian.


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