Hello and welcome to this lecture, where you were going to learn a laundering part and concept which
is cross over.
We can see in the complete diagram of the genetic algorithm that we are in this part here.
The crossover is considered a genetic operator.
It is important to emphasize that we are implementing each separate parts and after we will put it all
together.
Now let's look at some definitions.
Crossover combines parts of the chromosome from two bearings to generate a feeder child, so the population
tends to evolve and we can create diversity through these combination in our implementation.
We have only one individual, so we will need to create another individual to combine the chromosomes
to generate new individuals.
Remembering again, that the chromosome is this solution of the problem.
This sets off zeros and ones that indicate whether up products will be loaded on a truck to be easier
to understand.
You will learn how to use this genetic operator.
Let's suppose that we have two parents are individuals parent one, and parents chew with their corresponding
solutions.
For example, one zero one one zero one and zero and parents two zero one zero zero one one and one.
The arrows show the cut off point.
And here we have a new term, which is gene.
You have already learned that this sets off zeros and ones is called chromosome, while each value is
called gene.
For example, the first gene of parent one is one.
And the third gene of parents, two is zero.
The cut off points separate the genes.
Now in these two randomly selects a cut off point.
That's why the name here is one point two splits the chromosome.
Let's pick this point here.
We have genes on the left side and genes on the right sides.
Three here and four here.
And it doesn't matter in which genes you create the cutoff points.
For example, if it was here in this part, we would to have one gene on the left side and six genes
on the right sides.
If we select these good off point here, we would have one gene on the right side and six genes on the
left side.
Later on, we will implement a function to randomly selected.
Then next step is to generate two children, as you can see here, child one and child two.
For example, we can see that's child one is composed of the first bards of parent one, one, zero
and one.
And with this sequence, parts of parents two, zero, one, one and one.
And regarding child's chew, it is composed of the first part of parents.
Sure.
And with this sequence, parts of parent, when we have a kind of cross combining the chromosomes,
as we can see here, this part is combined it with these parts.
The left side of parent one is combined with the right side of parents chew and on the other two hands
before.
First parts of Paradise, Chu is combined with this second part of parents when we applied this operation
because the banned this is that's if we combine the solutions are the individuals our chromosomes will,
we will have bad results over two generations.
For example, this chu individuals here are from the first generation, while these other two are from
this second generation and evolution of the first generation.
And that's all for this lecture, where you will learn the theory behind the crossover and in the next
one will implement it.
See you there!