Optimization With Genetic Algorithms: Hands-On Python

Learn how to implement genetic algorithm from scratch to solve real world optimization problems

What you'll learn

Introduction to Genetic Algorithm Concepts

Development of Genetic Algorithm from scratch

Essential genetic operators used in genetic algorithms

Genetic Algorithm Library in Python

Requirements

No programming experience needed, you will learn everything you need to know!

Description

The "Optimization with Genetic Algorithms: Hands-on Python" course is a comprehensive and practical guide to understanding and implementing genetic algorithms for solving various optimization problems. Genetic algorithms, inspired by the principles of natural evolution, are powerful techniques for finding optimal solutions in multiple domains.In this course, you will learn the fundamental concepts of genetic algorithms and their applications in optimization. Starting from the basics, you will explore the principles of selection, crossover, and mutation that drive the evolution process. You will understand how to represent problem solutions as chromosomes, apply genetic operators to generate offspring, and evaluate the fitness of individuals.With a hands-on approach, you will dive into implementing genetic algorithms using Python programming language. Through a real-world problem project, you will gain proficiency in designing and optimizing genetic algorithms for real-world scenarios. You will learn how to define appropriate fitness functions, set up population structures, control algorithm parameters, and handle constraints in optimization problems.Throughout the course, you will explore different variations of genetic algorithms, including elitism, to enhance the optimization process. By the end of the course, you will have a strong foundation in genetic algorithms and be equipped with the skills to apply them to a wide range of optimization problems. You will be able to implement efficient and effective genetic algorithms in Python, analyze their performance, and make informed decisions for parameter tuning and problem-specific customization.Whether you are a student, programmer, researcher, or professional seeking advanced optimization techniques, this course will empower you to solve complex problems using genetic algorithms and unleash the power of optimization in your projects and applications.

Overview

Section 1: Introduction

Lecture 1 Course Content

Lecture 2 Course Information

Lecture 3 Resources

Section 2: Python Basics

Lecture 4 Note!

Lecture 5 Google Colab

Lecture 6 Variables and Methods

Lecture 7 Math Operators

Lecture 8 Assignment Operators

Lecture 9 Comparison Operators

Lecture 10 Logical Operators

Lecture 11 Conditional Statement

Lecture 12 Loop

Lecture 13 List

Lecture 14 Dictionary

Lecture 15 Tuple

Lecture 16 for Loop

Lecture 17 Range Function

Section 3: Introduction to Genetic Algorithm

Lecture 18 Biology Aspect of Genetic Algorithm

Lecture 19 Mathematical Aspect of Genetic Algorithm

Section 4: Developing Genetic Algorithm from Scratch

Lecture 20 Understanding the Problem

Lecture 21 Defining the Equations

Lecture 22 Initialization

Lecture 23 Evaluation

Lecture 24 Selection

Lecture 25 Crossover

Lecture 26 Mutation

Lecture 27 Elitism

Lecture 28 Finding the Optimal Solution

Lecture 29 Visualization

Lecture 30 Calling the Genetic Algorithm Function

Section 5: Genetic Algorithm Library

Lecture 31 Introduction

Lecture 32 Understanding the Problem

Lecture 33 Initial Installations

Lecture 34 Defining the Equations

Lecture 35 Creating an Instance and Run the Model

Lecture 36 Hyper Parameters

Lecture 37 Exercise

Section 6: Conclusion

Lecture 38 Conclusion and Future Reading Suggestion

Students pursuing degrees in computer science, engineering, mathematics, or related fields,Programmers and software developers who are interested in learning new algorithms and problem-solving techniques,Researchers in the fields of optimization, artificial intelligence, and evolutionary computation,Data scientists who work on optimization problems or seek alternative approaches to traditional optimization methods,Engineers and professionals working in domains such as manufacturing, logistics, finance, and operations,Individuals with a general interest in algorithms, optimization, and problem-solving