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The Ultimate Beginners Guide to Genetic Algorithms In Python

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The Ultimate Beginners Guide to Genetic Algorithms In Python Udemy Coupon

The Ultimate Beginners Guide to Genetic Algorithms In Python

Implement genetic algorithms from scratch to solve real world problems!

Created by Jones Granatyr, Edson Pacholok, IA Expert Academy | 5 hours on-demand video course

Genetic algorithms are an important area of ​​Artificial Intelligence responsible for solving complex real world problems. There are several practical applications of this type of algorithm, which can be applied to problem solving in everyday business situations. A classic example is solving the problem of teacher schedule in schools, in which there are different combinations of schedules and classes and the goal is to build the schedule dynamically according to the number of classes and the availability of each teacher. Other examples are: telecommunications companies can design new optical networks, carriers can better plan the delivery route for goods, investors can choose the best investments; among several others.

In this course, you will learn everything you need to enter the world of genetic algorithms! What makes this course unique is that you will learn the basic intuition and especially, the step-by-step implementation without using pre-built libraries. In other words, we are going to implement genetic algorithms from scratch using Python.

What you’ll learn

  • Learn in theory and practice the main concepts about genetic algorithms, such as: individual, population, crossover/reproduction, mutation, and evaluation
  • Implement genetic algorithms from scratch in Python
  • Implement a step-by-step genetic algorithm in Python to solve real world problems, such as the transport of products and optimization of flight schedule
  • Apply genetic algorithms to maximization and minimization problems
  • Visualize the genetic algorithm results using dynamic graphs
  • Integrate genetic algorithms with a database in MySql
  • Learn how to build genetic algorithms using DEAP and MLROSe libraries

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Josh Smith
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