Support for Minimal Fitness Function about neataptic HOT 3 OPEN

You are a right, the goal of all distance/error functions is to minimize their value. The built-in Network.prototype.evolve function tries to minimize the error on a certain dataset. A simple workaround is used:

return -sum;

As negative fitnesses are supported by Neataptic. From my perspective, I think it is quite logical that a 'fitness function' should return a higher value if a genome performs better (thus is 'fitter').

I think this is just a matter of personal preference. If more people bring this up then I will reverse the sort function, but still, this would create some problems with selection.FITNESS_PROPORTIONATE.

I could also create a seperate option when constructing the Neat instance where you can specify if you want to minimize or maximize the value of the fitness function.

from neataptic.

Thanks for the work around suggestion! I didn't even think about negating the absolute sum result. Thank you for dedicating your time towards working on this project.

I think your argument makes sense. For example, let's say you want to write a fitness function which maximizes the amount made in a stock market trade. You want the network which has the greatest value. But, in another example, let's say you want a network to predict a position on an image; it would make sense to have your fitness function sum to zero be the maximum.

At the very least, I'd say document that the current implementation of the fitness function expects a maximum value to be the "most fit". (maybe I just skipped over that part in the docs, but I didn't read anything like that)

from neataptic.

Exactly. It depends on the problem. For some problems it is easy to define a maximum score (e.g. distance = 0) but not a minimum score and for some problems it is easy to define a minimum score (e.g. goals scored = 0) but not a maximum score.

I have added this extra info in the develop branch (7fc2cb4) and it will be build soon. I hope that makes a little clearer. I still might implement an option that allows the choice for minimize/maximize score. Thanks for the appreciation 😄

from neataptic.