Genetic Algorithm: A to Z with Combinatorial Problems

This course on Genetic Algorithms (GA) is one of the most practical and comprehensive courses available, designed to provide an integrated framework for solving real-world optimization problems in the most straightforward manner. It is the first of its kind to offer a hands-on approach in the domain of metaheuristic algorithms, making it essential for students, researchers, and practitioners. The course begins with an introduction to the basic theory of GA, followed by the implementation of the simplest version of GA, the Binary GA, into Matlab.

It then progresses to the continuous version, the Real GA. The primary focus will be on the Genetic Algorithm, a highly regarded optimization algorithm in the literature. Subsequent sections will introduce well-known operation research problems such as transportation, hub location (HLP), quadratic assignment, and travelling salesman (TSP) problems, and demonstrate how to solve them using GA.

This approach will equip you with a comprehensive framework to tackle any combinatorial optimization problems. Additionally, the course will cover two renowned methods for tuning GA's parameters: the Taguchi method and the Response Surface Methodology (RSM). Finally, we will provide a statistical analysis using Minitab software and Design Expert to compare different metaheuristics effectively.

Key features of this course include:• Solving various challenging real-world problems• Managing penalty functions in real-world problems• Conducting comprehensive statistical analysis• Defining chromosomes for different problems• Handling algorithm parametersThe course includes a plethora of coding videos, providing ample opportunity to practice the theory covered in the lectures. It also features several real case studies, allowing you to learn the process of solving challenging problems using GA. Upon completing this course, you will be well-versed in implementing GA on a wide range of operation research problems in Matlab.