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Hello and welcome to this lecture, where we are going to implement an additional function should the

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genetic algorithm class and they have to implement it.

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We are going to put all records together and make some experiments and see the results of our implementation.

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We will create a new function bath visualise generation.

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It won't receive any parameter.

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And as the name suggests, the idea is to show the partial results of the algorithm, as we saw earlier.

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Our population is composed of several individuals, and then we need to evaluate the individuals and

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gather the best one.

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And in the next lecture, you'll learn how to create the new generation.

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However, we are going to run these codes a couple of times in order to create more than one or two

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generations.

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For example, we can create 50 generations, 100 generations, and so one.

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So this function will be used to present the best in the field well off each one of the generations.

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I will create a new variable cloud based, which will be equal to self thought population position zero

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because as we implement as before, we need to call this function here based in video.

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And then we set these attributes here based solution with the best individual of the generation.

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Now that we have the best individual in this variable, we can just bring in some informations.

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The first one, the generation let's access cell population position zero bytes generation.

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We can also visualize the total price.

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This is the value that we want to optimize.

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Let's select best thoughts qua evaluation.

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This space use of the drug fast dots used space and finally lets brands date chromosome based but chromosome.

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Now we can recreate the genetic algorithm glass, and we are not going to perform some tests using this

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function now because it will be done in the next lecture where we are going to put all these codes together.

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See you there!