Parameter optimisation operators offer different parameter settings

Andrew2

Hello,

I have noticed that the "Optimize Parameter (Grid)"  operator offers more parameters to vary for inner operators when compared to the "Optimize Parameter (Evolutionary)" operator.

For the k-NN operator, I am offered a full range of this operator's parameters when I use the Grid optimizer. When I use the Evolutionary operator the number of possibilities reduces.; it is not possible to vary the nominal measure type for example.

This has the effect that I can't take advantage of the evolutionary operator to find optimum parameter settings so I am slowed down with the brute force grid approach. It's not too bad - I go for a cup of tea while the process is running.

Anyhow, is there a subtle point I am missing that restricts available parameters in the evolutionary case?

regards

Andrew

IngoRM

Hi,

the reason is simple: the used optimization scheme is Evolutionary Strategies which works on real numbers only and simply cannot handle nominal values (and hence nominal parameters). For integers, we added a "hack" in order to support them as well but for nominal numbers this is not really possible. In general, the mixture of nominal and numerical values is not the best idea for evolutionary algorithms and best results would be achieved by transforming the nominal parameters into binominals before which can sometimes blow up the search space a lot. Otherwise, the assumption that smaller mutations should be more probable than larger ones would no longer hold.

Cheers,
Ingo