🚀 go-pugleaf

"Daniel Dyer" <dan@dannospamformepleasedyer.co.uk> wrote in message 
news:op.s0yyt3kqgnpvuy@cgl0517.chaucer.co.uk...
> On Mon, 28 Nov 2005 20:53:40 -0000, Oliver Wong <owong@castortech.com> 
> wrote:
>>     What you'll want to do is to programmatically enter in every strategy
>> you've tried at hooking up with a girl. You'll need to have some metric 
>> for
>> measuring success, and enter those in for each strategy as well. Then run
>> one step of simulation (i.e. one generation) so that the strategies will
>> have mutated slightly, with the strategies that have performed the best
>> mutating the least (thus, on average, your strategies have become 
>> "better").
>
> That's not quite how it works.  All strategies are equally eligible for 
> mutation, regardless of how good they are, and there's every chance the 
> next generation will be worse on average than the previous one. 
> Eventually however, if your selection strategy and fitness function are 
> good, the quality of strategies in your population should improve and will 
> tend towards an optimum (there are no guarantees though).

    Really? I thought a typical implementation would try to preserve the 
qualities of the most-fit strategy found. One way was the mutate the good 
strategies less (that's the one I mentioned above), and the other was to 
generate more "offsprings" from the good strategies.

    - Oliver