Genetic Algorithm: A to Z with Combinatorial Problems

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Posted on 04 Aug 13:21 | by rai10 | 23 views

Genetic Algorithm: A to Z with Combinatorial Problems
Size: 4.5 GB Type: eLearning Year: 2022 Version: 08/2022

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.5 GB | Duration: 97 lectures • 12h 9m

Learn how to implement Genetic Algorithn to solve real-world combinatorial optimization problems using Matlab

What you'll learn
Basic concepts and terms related to Genetic Algorithm (GA)
Basic rules of Matlab programming which needed for implementing any metaheuristic
Apply Genetic Algorithm for a wide range of operation research problems
Determine best values for Genetic Algorithm parameters using two famous methods
Statistical analysis for comparing metaheuristics

Requirements
Basic knowledge in programming
Basic knowledge in Operations Research and Optimization - (not a must, but helpful)
Basic knowledge in statistical analysis - (not a must, but helpful)
Description
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 simplest 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.

Who this course is for
Anyone who wants to learn Genetic Algorithm
Those who wants to solve operation reaserch problems with Genetic Algorithm
Anyone who wants to code Genetic Algorithm in Matlab
Anyone who wants to compare two metaheuristics statistically