How to approach genetic algorithms, neural networks, the beginnings of AI in general to this area

Question

Neural networks

In general, I have been doing programming for about 2-3 years, of which a year has been serious. During this year, I quite well (in my very unbiased opinion) studied c #, as well as the basics of algorithmization, but the Skit was postponed, Albahari got tired somehow, but I want to do something.
By coincidence, in addition to programming, I have another hobby - evolutionary genetics. Once leafing through a long-read book by Dawkins, I came across how he modeled evolutionary processes, and now I wanted to invent a bicycle to try myself. Of course, nothing good came of it, only a lot of hours spent. But just recently I heard "Genetic Algorithms" - the name interested me, and ...
Here is a set-up, nowhere I found a clear AND detailed explanation, it was either huge mathematical gibberish (meaning completely, i.e. I am for the moderate use of mathematics, but here you must not overdo it) or something at the level: "Here, here this, well, copy it here and this is it, that's it, now copy this code."
On one forum I found this , but unfortunately I did not find it in electronic form.
It seems to me that there are people here who work or are fond of this area, recommend where to start for a completely newbie.
PS Maybe I'm looking in the wrong place, ie. I'm interested in exactly the imitation of behavior, evolution on a microscale, as I understand genetic algorithms, as many people write - just a way of optimization, but from the articles that I read, it seems to me that they can be used to model evolution, or not? What then follows?

Answer 1

I perceive genetic algorithms as a way of optimization.
For evolution simulation I would look at some simulation solutions like Beagle

Answer 2

"We program the collective mind"
www.ozon.ru/context/detail/id/4877842
Minimum of mathematics, maximum of practice.