## ECJ-INTEREST-L@LISTSERV.GMU.EDU

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 Subject: Re: ecsuite examples From: Christopher Vo <[log in to unmask]> Reply To: ECJ Evolutionary Computation Toolkit <[log in to unmask]> Date: Sat, 16 Feb 2008 22:01:18 -0500 Content-Type: text/plain Parts/Attachments: text/plain (43 lines)
```Martin V. wrote:

> - Do all of 7 functions have minimum at 0.0?
Many of the functions in ECSuite are inverted so that ECJ can perform
maximization instead of minimization. You can see how the functions are
implemented and look at the fitness of the ideal individuals inside the
evaluate() method in ec/app/ecsuite/ECSuite.java.

There you can see that with the Rosenbrock, Rastrigrin, Sphere, and Step
problem types, an individual's fitness is ideal when the fitness is
equal to 0.0f. Noisy quartic, Booth, and Griewangk have no ideal fitness
value defined.
> - What is the probability of crossover (one-type)? No parameter is
> specified in ecsuite.params, like the one for mutation probability
> (0.005).
VectorCrossoverPipeline (which is used in ecsuite.params) grabs
individuals from the two sources and always performs the crossover. Note
that there also exists a parameter
"pop.subpop.0.species.crossover-prob", however it is only for use with
any-point crossover.
> - How can I find ideal individual (minimum of the function), do I need
> to change some parameters? Now for rastrigin (default function) I find
> the best individual have value of about -235 (towards zero is better).
> I have run the evolution few times with random seed but with no luck
> to get fitnes towards 0. Maybe I need to change mutation probability,
> the number of generations, the number of individuals, the size of the
> genome...etc.?
Rastrigin is a function with a lot of local minima. You will have to do
your own experimentation with the various parameters to find what
parameters work well. Note that with floating point vectors, you may not
be able to get exactly 0.0, but approximately 0.0. Actually, in my
experience, if you see "Found Ideal Individual" on one of the ECSuite
problems, it is often an indicator that something went wrong...

Try increasing the population size (pop.subpop.0.size). Also, just to
make sure things are working, you may just want to try the same function
with a smaller genome size. For example, try 10 genes instead of 50. You
can adjust the genome size via the genome-size parameter. You might also
try experimenting with Gaussian mutation instead of reset mutation for
this problem.

--Chris Vo
```