Discussion of article "Population optimization algorithms: Saplings Sowing and Growing up (SSG)" - page 7
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but still, I think, finding such points had a probabilistic character.
My message is that there should be no input parameters requiring "optimisation" at the TC input. Such parameters turn the TS into a coin.
Even internal self-optimisation of internal parameters is also self-deception.
I admit that there is food that doesn't need to be cooked. But I have not been able to discover such food. And cooked food still allows you to not be hungry. Yes, it's not perfect, but it's better than nothing.
The last 100 records will not correspond to different peaks, they will all be centred around one last found peak
The last 100 records will not correspond to different peaks, they will all be centred around one last peak found
Almost like that (depends on the algorithm). This last peak will be the "most" - global. It will be discarded for further optimisations.
This way we will get a list of peaks: from Everest to the hill.
I suppose there are foods that don't need to be cooked. But I've never been able to discover such food. And boiled food does keep you from being hungry. It's not perfect, but it's better than nothing.
illusionary food creates the illusion of satiety
but still, I think, finding such points
Even internal self-optimisation of internal parameters is also self-defeating.
illusory food creates the illusion of satiety
This was a pretty good branch. Let's not spoil it.
Almost so (depends on the algorithm). This last peak will be the "most" - global. It will be discarded for subsequent optimisations.
This was a pretty good thread. Let's not spoil it.
I realise it looks like I'm trolling. But I'm actually saying a very important thing and trying to save you time.
If it works, then one optimisation + clustering of results will be enough.