Genetic Algorithm: A To Z With Combinatorial Problems

Learn how to apply Genetic Algorithn into real-world operation reserach problems

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 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. Consequently, you will be equipped to apply different metaheuristic algorithms to solve various problems.This course is not just a theoretical journey; it is a practical guide to mastering the application of Genetic Algorithms to real-world challenges. Equip yourself with the knowledge and skills required to excel in the field of operations research by enrolling in this course today.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Matlab Software

Lecture 3 Variables

Lecture 4 Arithmatic operations

Lecture 5 Relational operations

Lecture 6 Vector

Lecture 7 Matrix

Lecture 8 08-Indexing

Lecture 9 Matrix Operations

Lecture 10 Generating matrix

Lecture 11 Min,Max,Sort

Lecture 12 If Condition

Lecture 13 Rand functions

Lecture 14 Loop

Lecture 15 Plot

Lecture 16 Function

Section 2: Genetic Algorithm

Lecture 17 GA Inspiration

Lecture 18 Optimization Problem

Lecture 19 Starting with BGA

Lecture 20 Problem Definition

Lecture 21 Define Parameters

Lecture 22 Initialization

Lecture 23 Sorting Solutions

Lecture 24 Main loop and single point crossover

Lecture 25 Mutation

Lecture 26 PreparePopulation for NextGeneration

Lecture 27 Improving Crossover

Lecture 28 Improving Mutation

Lecture 29 Improving Selection Procedure

Lecture 30 Real GA

Section 3: Hub location problems

Lecture 31 An Introduction To Hub Location Problem

Lecture 32 Main Steps To Connect Problems To Metaheuristic

Lecture 33 How To Create Model

Lecture 34 Create Random Solution

Lecture 35 Defining Cost Function

Lecture 36 Connecting Cost Function To BinaryGA

Lecture 37 Visualization The Solution

Section 4: Transportation

Lecture 38 An Introduction To Transportation Model

Lecture 39 Generate Problems

Lecture 40 Defining Chromosome

Lecture 41 Implementation Chromosom In Matlab

Lecture 42 Penalty Function Explanation

Lecture 43 Measuring Cost Functions

Lecture 44 Connecting Problem To RealGa

Lecture 45 The Explaination of New Trasnportation Model

Lecture 46 Createing New Trasnportation Model

Lecture 47 Createing New Solution Representation

Lecture 48 Creating New Parse Solution

Lecture 49 Modifying Crossover

Lecture 50 Modifying Mutation

Lecture 51 Modifying Cost Function

Lecture 52 Connecting New Problem To RealGa

Section 5: Quadratic assignment problem

Lecture 53 An Introduction To QAP

Lecture 54 Creating QAP Model

Lecture 55 Solution Representation For QAP

Lecture 56 Coding Solution Representation For QAP

Lecture 57 Cost Function For QAP

Lecture 58 Crossover For QAP

Lecture 59 07-Appied Crossover For QAP

Lecture 60 Mutation For QAP

Lecture 61 Mutation Code For QAP

Lecture 62 Connetcing QAP to GA

Lecture 63 Plotting QAP

Section 6: Knapsack Problem

Lecture 64 An Introduction To Knapsack Problem

Lecture 65 Create Parameters

Lecture 66 Solution Representation

Lecture 67 Coding Solution Representation

Lecture 68 Penalty Function Strategies

Lecture 69 Coding Cost Function

Lecture 70 Connecting Knapsack Problem to GA

Section 7: Traveling Salesman Problem

Lecture 71 An Introductio to Traveling Salesman Problem

Lecture 72 Create Random Model

Lecture 73 Create and Save Models

Lecture 74 Create Random Solution

Lecture 75 Cost Function for TSP

Lecture 76 Crossover for TSP

Lecture 77 Coding Crossover for TSP

Lecture 78 Mutation for TSP

Lecture 79 Coding Mutation for TSP

Lecture 80 Connecting TSP to GA

Lecture 81 Visualization

Lecture 82 New TSP model

Section 8: Experiment Design

Lecture 83 An Introduction To Tuning Metaheuristics

Lecture 84 Normalization Objective Functions

Lecture 85 Taguchi Method

Lecture 86 Identifying Parameters

Lecture 87 Determing levels of Parameters

Lecture 88 Determining orthogonal array

Lecture 89 Carrying Out Experiments

Lecture 90 Anlyzing Experiments

Lecture 91 RSM Method

Lecture 92 Identifying Parameters in RSM

Lecture 93 Determing design Experiment for RSM

Lecture 94 Carrying Out Experiment in RSM

Lecture 95 AnlyzingExperiment of RSM

Section 9: Statistical Test

Lecture 96 An Introduction Statistical Anlysis

Lecture 97 Implementing WilcoxonTest Rank for comparing algorithms

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