Hello and welcome to this lecture, where we are going to implement an additional function should the
genetic algorithm class and they have to implement it.
We are going to put all records together and make some experiments and see the results of our implementation.
We will create a new function bath visualise generation.
It won't receive any parameter.
And as the name suggests, the idea is to show the partial results of the algorithm, as we saw earlier.
Our population is composed of several individuals, and then we need to evaluate the individuals and
gather the best one.
And in the next lecture, you'll learn how to create the new generation.
However, we are going to run these codes a couple of times in order to create more than one or two
generations.
For example, we can create 50 generations, 100 generations, and so one.
So this function will be used to present the best in the field well off each one of the generations.
I will create a new variable cloud based, which will be equal to self thought population position zero
because as we implement as before, we need to call this function here based in video.
And then we set these attributes here based solution with the best individual of the generation.
Now that we have the best individual in this variable, we can just bring in some informations.
The first one, the generation let's access cell population position zero bytes generation.
We can also visualize the total price.
This is the value that we want to optimize.
Let's select best thoughts qua evaluation.
This space use of the drug fast dots used space and finally lets brands date chromosome based but chromosome.
Now we can recreate the genetic algorithm glass, and we are not going to perform some tests using this
function now because it will be done in the next lecture where we are going to put all these codes together.
See you there!