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Hello and welcome to this lecture, where you were going to learn a laundering part and concept which

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is cross over.

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We can see in the complete diagram of the genetic algorithm that we are in this part here.

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The crossover is considered a genetic operator.

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It is important to emphasize that we are implementing each separate parts and after we will put it all

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together.

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Now let's look at some definitions.

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Crossover combines parts of the chromosome from two bearings to generate a feeder child, so the population

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tends to evolve and we can create diversity through these combination in our implementation.

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We have only one individual, so we will need to create another individual to combine the chromosomes

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to generate new individuals.

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Remembering again, that the chromosome is this solution of the problem.

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This sets off zeros and ones that indicate whether up products will be loaded on a truck to be easier

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to understand.

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You will learn how to use this genetic operator.

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Let's suppose that we have two parents are individuals parent one, and parents chew with their corresponding

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solutions.

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For example, one zero one one zero one and zero and parents two zero one zero zero one one and one.

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The arrows show the cut off point.

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And here we have a new term, which is gene.

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You have already learned that this sets off zeros and ones is called chromosome, while each value is

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called gene.

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For example, the first gene of parent one is one.

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And the third gene of parents, two is zero.

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The cut off points separate the genes.

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Now in these two randomly selects a cut off point.

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That's why the name here is one point two splits the chromosome.

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Let's pick this point here.

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We have genes on the left side and genes on the right sides.

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Three here and four here.

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And it doesn't matter in which genes you create the cutoff points.

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For example, if it was here in this part, we would to have one gene on the left side and six genes

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on the right sides.

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If we select these good off point here, we would have one gene on the right side and six genes on the

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left side.

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Later on, we will implement a function to randomly selected.

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Then next step is to generate two children, as you can see here, child one and child two.

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For example, we can see that's child one is composed of the first bards of parent one, one, zero

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and one.

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And with this sequence, parts of parents two, zero, one, one and one.

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And regarding child's chew, it is composed of the first part of parents.

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Sure.

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And with this sequence, parts of parent, when we have a kind of cross combining the chromosomes,

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as we can see here, this part is combined it with these parts.

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The left side of parent one is combined with the right side of parents chew and on the other two hands

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before.

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First parts of Paradise, Chu is combined with this second part of parents when we applied this operation

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because the banned this is that's if we combine the solutions are the individuals our chromosomes will,

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we will have bad results over two generations.

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For example, this chu individuals here are from the first generation, while these other two are from

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this second generation and evolution of the first generation.

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And that's all for this lecture, where you will learn the theory behind the crossover and in the next

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one will implement it.

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See you there!