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In this video, I explained Genetic Algorithm (GA), its important points, flow with examples. I prepared this video for the beginners of Genetic Algorithm, and tried to show a big picture. As a use case, I selected Travelling Salesman Problem (TSP) and explained GA operators and flow steps based on this example. You can use these principles for different optimisation problems. In the second part, I will show Python Implementation for the TSP with Genetic Algorithm. I will explain codes and techniques that I used one by one. So, you can use same principles and techniques for your project. Timecode 0:00 Intro 0:18 Video content - presentation outline 0:33 Evolutionary Approach 1:48 Evolutionary Algorithms/Presentations 2:05 Introduction to Genetic Algorithm 3:18 Genetic Algorithm Operators and General Flow 4:54 Genetic Algorithm Fundamentals - Chromosome Structure 6:10 Genetic Algorithm Flow - Steps with Examples 8:22 GA Flow - Initialisation and Selection 8:54 GA Flow - Crossover with an example (one points crossover) 10:43 GA Flow - Mutation with an example 11:37 Fitness Function and Fitness Value - TSP Example 13:22 Why we need GA? What is the problem? 15:48 Route Planning - Travelling Salesman Problem 17:05 Closing - Next Video Info(Python Implementation) #geneticalgorithm #python #geneticalgorithmpython #tspwithpython #travellingsalesmanproblem #routeplanning