Hello and welcome to this lecture, where you are going to learn the differences between evolutionary
algorithms and genetic algorithms.
These are two very common terms in this area, and they are often confused as if they were the same.
However, there is a small difference between them.
That is very important, you know?
Now let's quickly look at some definitions.
First, we have the category of evolutionary algorithms, which is composed of computational models
that represent the natural processes of evolution.
This simulation of species, the survival of the fittest, are the best individual.
And also there are some other areas that are related to self-organization and adaptive behavior.
It is important to emphasize that these algorithms are part of a large in Marica area than genetic algorithms,
as if it were the root of the area.
So genetic algorithms are parts of evolutionary algorithms.
As you can read, the here genetic algorithms are a branch of evolutionary algorithms, and the main
idea is who gets bad and bad better solutions through the evolution of previous generations.
Basically, we built an initial generation and apply evolution processes like what happens in the real
world where the new generations are more evolved than past generations.
In short, genetic algorithms are more related.
Should these sand philosophy here, while evolutionary algorithms have more general concepts, this
image shows the entire flow of genetic algorithm that we are going to implement step by step.
First, we create the initial population and then we evaluate it.
Here we have this stop being crit burial, which indicates how long we are going to run the algorithm.
In general, we defined the number of generations that will be created, and we run the algorithm based
on that number.
For example, if we define 100 generations, we will run this entire piece of steps one hundred times.
And the algorithm and so here, when the number of generations equals 100, it is similar to a far loop
in programming languages.
Every time the algorithm will through the diamonds, we construct a new generation and we perform all
these steps.
Select parents crossover mutation, crossover and mutation are called genetic operators.
As you can read here, and the old population is discarded, we then evaluate the new population.
They find this surviving population and return to these in need shows their behavior at the hands of
the process.
We simply list the best individuals are the best solutions.
This lecture was just to show you the general structure of a genetic algorithm.
And don't worry about understanding all the steps now because we are going to analyze and implement
each of them in detail in the next lecture.
You will understand that the problem that we are going to solve and soon after where we started the
implementations, so there.