I don’t understand your second question, but I can shed some light on the first.

To start with you need to understand that computers are dumber than the bricks we build with. The can’t think in any practical terms - in fact they can’t think at all. Even the best AIs are just trying an input, evaluating an error, and repeating the process until the result is accepted.

Next it helps to know a bit about how Generative Design works. Effectively the application is solving complex equations via trial and error. In randomize the values are completely random, sampling N times and you hope that the solution is found. In optimize a population of N samples are taken and the highest performing solutions inform the next round of samples, this is repeated for M generations.

Say you needed to pick find the right value for X in the equation X + Y = Z. You have no idea what Y is until you do the math and it changes each time, and the goal is to minimize the value of Z.

In the first test you selected 0 to be the value of X, and Z wound up being 100. Is this good or bad? Well obviously we don’t know yet, so let’s try again with 10 and get 99.5. Then again with 20 and get 90.153. Going bigger is helping the score, so you’d want to test something huge - say 1,000,000,000,000,000,000,000,000, which returns a score of 1000. Now you know at some point between 20 and the absurdly large number is an optimum , but there are an absurdly large amount of values to test. The number of samples required to narrow in on a solution is absurd, and this doesn’t ensure the optimum, but just one optimum value. There could be another better fit above 1e24, and our four samples hardly scratch the surface of the scope. What number would you guess next? Do you think there would there be any chance of finding the optimum before your deadline?

This problem gets worse, as we haven’t considered -10, -20, or -1,000,000,000,000,000,000,000,000 yet. And while I have been sticking to integers so far, the computer doesn’t know to not test both 10 and 10.00000000000000000001. The amount of values to test is literally infinite.

Fortunately we as designers know what values make sense to use in our work. 1*10^24 isn’t a feasible panel size, room count, or other value, and so we can set the upper limit (and the lower limit and step value) according to what we know about the context of the problem, such as the size of bricks, the maximum dimension of panels, the most number of desks the customer could ever want, how many rooms are too many, etc… The minimum, maximum, and step values of a slider allow us as designers to shape the limits of the testing range in order to prevent the CPU from saying “The best way to build this tower will require a flying buttress based from Mars.”