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Those are completely disparate guidelines that you can reduce into one, neat, tidy cost function for a route by applying different weights to each parameter. Mating is an undeniable fact of life, and it can be used by us tons in GAs. Mating is a magical moment that two chromosomes deeply in love with one another share.

The specialized term for mating is “crossover”, but I’ll continue to call it “mating” here, because that paints a more intuitive picture. We haven’t talked about “populations” in GAs yet (we’ll get to that a bit later) but for now I’ll just say that when you run a GA, you do not just take a look at one chromosome at the same time.

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You may have people of 20 or 100 or 5,000 heading all at one time. Like in evolution Just, you may be inclined to have the best and most powerful chromosomes of the population partner with one another, with the hope that their offspring will be healthier than either parent even.

Mating strings, like in our “Hello, World!” example is pretty easy. You can pick two candidates (two strings; two-chromosome) and select a point in the middle of the string. This true point can be dead-center, if you want or randomized if you prefer. Take that middle point (called a “pivot” point), and make two new chromosomes by combining the first half of one with the second half of the other and vice versa.

Mating is how you get from one generation of genes to another. Mating by itself has a problem: in-breeding. If all you do is partner your candidates to look from generation to generation, you’ll get stuck near a “local optimum”: an answer that’s very good but not necessarily the “global optimum” (the best you can expect). I was up to date, that the above-mentioned paragraph is misleading recently.