Optimization with Genetic Algorithms: Hands-on Python

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Navid Shirzadi

4:34:37

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1. Introduction

1. Course Content.mp4

05:47

2. Course Information.mp4

03:08

3.1 Source-Codes.zip

3. Resources.html

2. Python Basics

1. Note!.html

2. Google Colab.mp4

09:10

3. Variables and Methods.mp4

13:42

4. Math Operators.mp4

03:09

5. Assignment Operators.mp4

03:52

6. Comparison Operators.mp4

02:23

7. Logical Operators.mp4

04:30

8. Conditional Statement.mp4

08:05

9. Loop.mp4

04:44

10. List.mp4

14:45

11. Dictionary.mp4

06:55

12. Tuple.mp4

04:08

13. for Loop.mp4

03:39

14. Range Function.mp4

03:15

3. Introduction to Genetic Algorithm

1. Biology Aspect of Genetic Algorithm.mp4

09:24

2. Mathematical Aspect of Genetic Algorithm.mp4

07:40

4. Developing Genetic Algorithm from Scratch

1. Understanding the Problem.mp4

05:38

2. Defining the Equations.mp4

06:45

3. Initialization.mp4

14:52

4. Evaluation.mp4

10:26

5. Selection.mp4

15:51

6. Crossover.mp4

17:57

7. Mutation.mp4

14:33

8. Elitism.mp4

04:38

9. Finding the Optimal Solution.mp4

10:23

10. Visualization.mp4

04:41

11. Calling the Genetic Algorithm Function.mp4

13:38

12. Hyper Parameters.html

5. Genetic Algorithm Library

1. Introduction.mp4

04:00

2. Understanding the Problem.mp4

03:16

3. Initial Installations.mp4

04:06

4. Defining the Equations.mp4

09:14

5. Creating an Instance and Run the Model.mp4

09:11

6. Hyper Parameters.mp4

13:47

7.1 exercise solution.zip

7. Exercise.html

6. Conclusion

1. Conclusion and Future Reading Suggestion.mp4

03:25

Description

Learn how to implement genetic algorithm from scratch to solve real world optimization problems

What You'll Learn?

Introduction to Genetic Algorithm Concepts
Development of Genetic Algorithm from scratch
Essential genetic operators used in genetic algorithms
Genetic Algorithm Library in Python

Who is this for?

Students pursuing degrees in computer science, engineering, mathematics, or related fields

Programmers and software developers who are interested in learning new algorithms and problem-solving techniques

Researchers in the fields of optimization, artificial intelligence, and evolutionary computation

Data scientists who work on optimization problems or seek alternative approaches to traditional optimization methods

Engineers and professionals working in domains such as manufacturing, logistics, finance, and operations

Individuals with a general interest in algorithms, optimization, and problem-solving

What You Need to Know?

No programming experience needed, you will learn everything you need to know!

More details

Description
The "Optimization with Genetic Algorithms: Hands-on Python" course is a comprehensive and practical guide to understanding and implementing genetic algorithms for solving various optimization problems. Genetic algorithms, inspired by the principles of natural evolution, are powerful techniques for finding optimal solutions in multiple domains.
In this course, you will learn the fundamental concepts of genetic algorithms and their applications in optimization. Starting from the basics, you will explore the principles of selection, crossover, and mutation that drive the evolution process. You will understand how to represent problem solutions as chromosomes, apply genetic operators to generate offspring, and evaluate the fitness of individuals.
With a hands-on approach, you will dive into implementing genetic algorithms using Python programming language. Through a real-world problem project, you will gain proficiency in designing and optimizing genetic algorithms for real-world scenarios. You will learn how to define appropriate fitness functions, set up population structures, control algorithm parameters, and handle constraints in optimization problems.
Throughout the course, you will explore different variations of genetic algorithms, including elitism, to enhance the optimization process.
By the end of the course, you will have a strong foundation in genetic algorithms and be equipped with the skills to apply them to a wide range of optimization problems. You will be able to implement efficient and effective genetic algorithms in Python, analyze their performance, and make informed decisions for parameter tuning and problem-specific customization.
Whether you are a student, programmer, researcher, or professional seeking advanced optimization techniques, this course will empower you to solve complex problems using genetic algorithms and unleash the power of optimization in your projects and applications.
Who this course is for:
Students pursuing degrees in computer science, engineering, mathematics, or related fields
Programmers and software developers who are interested in learning new algorithms and problem-solving techniques
Researchers in the fields of optimization, artificial intelligence, and evolutionary computation
Data scientists who work on optimization problems or seek alternative approaches to traditional optimization methods
Engineers and professionals working in domains such as manufacturing, logistics, finance, and operations
Individuals with a general interest in algorithms, optimization, and problem-solving

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My name is Navid Shirzaid and I am super excited that you are here to read this section!I am a researcher with more than 7 years of experience in the field of controlling integrated energy systems with extensive skill in using mathematical optimization strategies. I am also proficient in coding with Python and developing machine learning and deep learning models for different applications. I have several publications in the field of designing and control strategies of energy systems using machine learning, deep learning, and artificial intelligence.To Conclude, I am passionate about Data Science and Machine Learning, and Optimization applications in real-world problems and I really like to share my experience with you!

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