Genetic Algorithms: What They Are And How To Build One

[LeeAndro]

Published 7/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 522.76 MB | Duration: 1h 18m

A Bner's Guide to Genetic Algorithms

What you'll learn
Explain what a genetic algorithm is
Identify problems that a genetic algorithm can solve
Create a genetic algorithm in Python
Explain different recombination methods
Requirements
Bner Python knowledge
Description
Genetic algorithms are a powerful tool for solving complex problems where there isn't an obvious solution or way to test different solutions.​

In this course you will learn the ins and outs of genetic algorithms, how they are built, what makes them tick, and how to design one for yourself. This is not just another lecture-based course where I teach you a bunch of theory and expect that you can implement it yourself. I go over every single step of creating a simple genetic algorithm from scratch in Python, demonstrating all of their elements as we build it to ensure that you understand how everything works together as a single unit. By the end of this course you will have developed a strong understanding about genetic algorithms, how they work and how to use them to build solutions for your own problems. By learning this technique you'll have an edge over your competition as more companies start using algorithmic approaches even for simple decisions. This course is aimed at anyone who wants to understand, learn and apply simple genetic algorithms to solve problems for real-world applications. The course assumes prior experience with Python, and you will be able to immediately start applying the concepts learned in your own projects.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Example Implementation

Lecture 2 Scenario Definition

Lecture 3 Item Class

Lecture 4 Individual Class

Lecture 5 Genetic Algorithm Class

Lecture 6 Test Run

Lecture 7 Comparison Setup

Lecture 8 Alternative Crossover Methods

Lecture 9 Comparison Test Run

Developers of any programming language curious about alternatives to Neural Networks,Developers that may have never heard of Genetic Algorithms,Developers looking to add more tools to their arsenal,Anyone looking to solve a combinatorial problem

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