Genetic Algorithm (GA): A mega course

This is one of the most applied courses on Genetic Algorithms (GA), which presents an integrated framework to solve real-world optimization problems in the most simple way. For the first time, we have presented a practical course in the domain of metaheuristics algorithms required for students, researchers and practitioners.   Firstly, we will introduce the basic theory of GA, then implement the simplest version of GA, namely Binary GA, into Matlab, and then present the continuous version, real GA, of it. Therefore, the main focus will be on the Genetic Algorithm as the most well-regarded optimization algorithm in the literature. In the following sections, we will introduce some well-known operation research problems, including transportation problems, hub location problems (HLP), quadratic assignment problems and travelling salesman problems (TSP) and try to solve them via GA. Therefore, we will provide you with a comprehensive framework to handle any combinatorial optimisation problems. We also offer two well-known methods for tuning GA's parameters, including the Taguchi method and response surface methodology(RSM). In the end, we provide statistical analysis to compare different metaheuristics effectively. Therefore, for the first time, the following important points are included in this course:

• Solving  different challenging real-world problems

• Handling penalty function in  real-world problems

• Comprehensive statistical analysis using Minitab software and Design Expert

• Defining chromosomes for different problems

• Handling algorithm's parameters

This course also includes a large number of coding videos to give you enough opportunity to practice the theory covered in the lecture. There are also several real case studies including real-world problems that allow you to learn the process of solving challenging problems using GA.

By passing this course, you will aware of how to implement GA on a wide range of OR problems in Matlab, and as a result, you will learn how to apply different metaheuristics algorithms to solve various problems.