FASTTRACK Optimization with Genetic Algorithm Hands-On Guide

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Dipl.-Ing. (M. Sc.) Muhammed Alp

5:37:01

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1. Introduction

1. What is this Course about.mp4

04:12

2. Greeting & Important Information.mp4

03:41

3. What is Optimizaiton.mp4

18:56

4. What is Genetic Algorithm (GA) based Optimization.mp4

03:46

2. GA Optimization Terminology

1. Terminology Group 1.mp4

16:36

2. What is a Pareto Front.mp4

18:47

3. Terminology Group 2.mp4

09:01

4. What is the Tournament Method.mp4

08:38

5. Quiz Check Your Facts on Genetic Algorithm Optimization Terminology.html

3. The GA Optimization Workflow Architecture

1. How Does a GA based Optimizer Workflow Look Like.mp4

07:11

4. Hands-On GA Optimization - The Python Way

1. Overview Multi-Parameter Optimization Validation Example.mp4

03:19

2. Optimization Set-Up.mp4

09:21

3. Initialize Population.mp4

13:49

4. Evaluate Initial Population.mp4

10:06

5. The Stopping-Criteria.mp4

14:19

6. Performance Calculation.mp4

35:26

7. Selection of Individuals.mp4

18:32

8. Create New Generation.mp4

37:18

9. Evaluate New Generation.mp4

09:02

10. The Stopping-Criteria Part 2.mp4

02:47

11. Iteration Loop + Results Discussion.mp4

20:10

12. Variation of Optimization Set-up Parameters.mp4

20:39

13. Bitwise Chromosomes - Knapsack Problem.mp4

34:38

14. Logistics Problem.mp4

15:08

15. Summary.mp4

01:39

5. Materials & GA Tool

1.1 Nuvaya Academy GA Series Course1.pdf

1. Slides.html

2.1 ga course 1 example1 v1.zip

2. GA Tool Validation Example.html

3.1 ga course 1 knapsack v3.zip

3. GA Tool Bitwise Chromosomes - Knapsack.html

4.1 ga course 1 truck transport v3.zip

4. GA Tool Bitwise Chromosomes - Logistics.html

Description

A Complete Beginners Guide To Genetic Algorithm Based Optimization with Free To Use Python Based Optimization Tool

What You'll Learn?

UNDERSTAND: Understand Basics of Genetic Algorithm Based Optimization
SPEED: Short Time-To-Application is number one goal of this course
APPLY: Be Able To Directly Apply To Your Individual Multi-Parameter Optimization Problem
TOOL: Get Nuvaya Technologies' ready-to-use Python based Genetic Algorithm Tool For Multi-Parameter Optimization Problems
TRANSFER: Apply Genetic Algorithm Based Optimization To Real-World Problems

Who is this for?

Engineers

Data Scientist

Engineering Manager

Python Developer

What You Need to Know?

Basic Programming Skills

More details

Description
Time-to-application is number one goal of this course --> After the course you can directly start optimizing using a ready-to-use Python based Genetic Algorithm Tool!
No time consuming "tool development from scratch" --> we work with ready-to-use, flexible Genetic Algorithm Optimization Tool written in the most basic Python and you get the tool at the end of the course
For all the "Beginners" who want to be real-world users in the most effective way possible
No advanced Python or programming skills needed --> Most basic Python is used for the whole algorithm --> Python lists and Numpy arrays thats it!
Designed for Beginners who don't want to program the algorithm or spent a lot of time to transfer someones hard-coded program to their individual optimisation problems
Complete Beginners Guide to Genetic Algorithm Optimization

Do you want to learn about a very powerful optimization method that is used for many optimization problems such as neural networks, engineering, logistics, finance and many more? Do you want to avoid a ton of theory without practical application or very specific code snippets that are not really transferable to your individual optimization problems?
If so, this course will help you enhance your optimization skills with:
- Genetic Algorithm based Optimization
- A ready-to-use python based simple but flexible Genetic Algorithm Based Optimizer

Genetic algorithm based optimization is a metaheuristic optimization method for a large specturm of optimization problems with multiple design parameters (multi-parameter). Compared to other optimization methods it is more stable against local extrema and offers great flexibility.
This course is designed so that you can apply GA optimization as fast as possible to your problems. A tool is provided that out-of-the-box is already able to solve many types optimization problems without the need to start from scratch or modify a lot.Â Â

This course offers:
A complete guide to Genetic Algorithm based Optimization for beginners
Almost no preliminary knowledge in programming and optimization required
Optimizer example build with most basic Python programmingÂ
Easy-to-understand step-by-step guide to Genetic Algorithm based Optimization
Practical knowledge, no dry theory
Hands-on step-by-step optimization validation case
Hands-on solving of real-world optimization problem
Ready-to-use, free GA based Optimization Tool based on python, which is not hard-coded and therefor flexible and usable for your multi-parameter optimization problems
Who this course is for:
Engineers
Data Scientist
Engineering Manager
Python Developer

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Mr. Alp holds a master of science (MSc) degree in aerospace engineering from the University of Stuttgart, Germany with majors in thermodynamics and fluid mechanics. Furthermore, Mr. Alp has 10+ years of industry experience in a variety of high-tech braches such as automotive and aerospace in different job positions. He is the founder and managing director of the Germany based technology company Nuvaya Hendese Technologies GmbH. Besides his Job at Nuvaya, Mr. Alp is a lecturer at the Cooperative State University Karlsruhe in Germany for the study program Sustainable Science Technology in the subjects thermodynamics and fluid dynamics.

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