Genetic Algorithm: A to Z with Combinatorial Problems

Course Curriculum

Chapter 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

Chapter 2: Genetic Algorithm

Lecture 1: GA Inspiration

Lecture 2: Optimization Problem

Lecture 3: Starting with BGA

Lecture 4: Problem Definition

Lecture 5: Define Parameters

Lecture 6: Initialization

Lecture 7: Sorting Solutions

Lecture 8: Main loop and single point crossover

Lecture 9: Mutation

Lecture 10: PreparePopulation for NextGeneration

Lecture 11: Improving Crossover

Lecture 12: Improving Mutation

Lecture 13: Improving Selection Procedure

Lecture 14: Real GA

Chapter 3: Hub location problems

Lecture 1: An Introduction To Hub Location Problem

Lecture 2: Main Steps To Connect Problems To Metaheuristic

Lecture 3: How To Create Model

Lecture 4: Create Random Solution

Lecture 5: Defining Cost Function

Lecture 6: Connecting Cost Function To BinaryGA

Lecture 7: Visualization The Solution

Chapter 4: Transportation

Lecture 1: An Introduction To Transportation Model

Lecture 2: Generate Problems

Lecture 3: Defining Chromosome

Lecture 4: Implementation Chromosom In Matlab

Lecture 5: Penalty Function Explanation

Lecture 6: Measuring Cost Functions

Lecture 7: Connecting Problem To RealGa

Lecture 8: The Explaination of New Trasnportation Model

Lecture 9: Createing New Trasnportation Model

Lecture 10: Createing New Solution Representation

Lecture 11: Creating New Parse Solution

Lecture 12: Modifying Crossover

Lecture 13: Modifying Mutation

Lecture 14: Modifying Cost Function

Lecture 15: Connecting New Problem To RealGa

Chapter 5: Quadratic assignment problem

Lecture 1: An Introduction To QAP

Lecture 2: Creating QAP Model

Lecture 3: Solution Representation For QAP

Lecture 4: Coding Solution Representation For QAP

Lecture 5: Cost Function For QAP

Lecture 6: Crossover For QAP

Lecture 7: 07-Appied Crossover For QAP

Lecture 8: Mutation For QAP

Lecture 9: Mutation Code For QAP

Lecture 10: Connetcing QAP to GA

Lecture 11: Plotting QAP

Chapter 6: Knapsack Problem

Lecture 1: An Introduction To Knapsack Problem

Lecture 2: Create Parameters

Lecture 3: Solution Representation

Lecture 4: Coding Solution Representation

Lecture 5: Penalty Function Strategies

Lecture 6: Coding Cost Function

Lecture 7: Connecting Knapsack Problem to GA

Chapter 7: Traveling Salesman Problem

Lecture 1: An Introductio to Traveling Salesman Problem

Lecture 2: Create Random Model

Lecture 3: Create and Save Models

Lecture 4: Create Random Solution

Lecture 5: Cost Function for TSP

Lecture 6: Crossover for TSP

Lecture 7: Coding Crossover for TSP

Lecture 8: Mutation for TSP

Lecture 9: Coding Mutation for TSP

Lecture 10: Connecting TSP to GA

Lecture 11: Visualization

Lecture 12: New TSP model

Chapter 8: Experiment Design

Lecture 1: An Introduction To Tuning Metaheuristics

Lecture 2: Normalization Objective Functions

Lecture 3: Taguchi Method

Lecture 4: Identifying Parameters

Lecture 5: Determing levels of Parameters

Lecture 6: Determining orthogonal array

Lecture 7: Carrying Out Experiments

Lecture 8: Anlyzing Experiments

Lecture 9: RSM Method

Lecture 10: Identifying Parameters in RSM