Hello and welcome to the first section of the cars on genetic algorithms in Python.
In this lecture, I'm going to show you the plan of attack, as you can read right here.
The goal of this section is to implement a genetic algorithm using Python without using any prebuilt
library.
We start from scratch and bite the hands of this section.
You will have a complete algorithm that can be used to solve real world problems.
Now, let's see the main topics that will be covered in this section.
First, you will when stands.
Some of the terminology in this area, such as the main differences between evolutionary algorithms
and genetic algorithms.
After you will understand all the theoretical and practical concepts about this topic, such as fitness
function, crossover mutation, population individuals and how you select the best individuals are solutions.
The approach of the lectures will be completely practical.
We will see some theoretical subjects and then we won't be amended step by step.
At the end, you will learn how to use the genetic algorithm that we will implement with a database
in my scale.
This topic is very important, mainly if you are developing software for a company and you need to access
data from one database.
So you are going to learn how to integrate genetic algorithms with commercial applications that sit
for this lecture in the next one.
We will learn some theoretical background and then we will move on to the implementations.
See you there!