Genetic Algorithms: What They Are and How To Build One скачать в хорошем качестве

Genetic Algorithms: What They Are and How To Build One

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. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ You can support my endeavors through: https://ko-fi.com/bocksdin_coding ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Complete code found here: https://github.com/RoryLetteney/genet... - - - - Music by Bensound.com/free-music-for-videos 00:00:00 Introduction 00:00:28 What is a "Genetic Algorithm"? 00:00:57 Gene Sequences 00:01:55 Benefits 00:02:53 Limitations 00:03:31 Possible Use Cases 00:04:05 Elements of Implementations 00:05:24 Steps of Implementations 00:07:28 Example Introduction 00:10:00 Item Class 00:13:12 Individual Class 00:16:15 Individual: Fitness Function 00:18:43 Individual: Single Point Crossover 00:22:24 Individual: Mutation 00:23:45 GeneticAlgorithm Class 00:25:00 GeneticAlgorithm: Initialize Population 00:28:55 GeneticAlgorithm: Select Best Individual 00:29:44 GeneticAlgorithm: Sum Values 00:30:35 GeneticAlgorithm: Select Parents 00:32:13 GeneticAlgorithm: Visual Generation 00:33:40 GeneticAlgorithm: Solve 00:42:03 Running / Testing 00:49:33 Alternative Crossovers Introduction 01:00:33 Alternative Crossovers: Two Point Crossover 01:05:45 Alternative Crossovers: Uniform Crossover 01:09:06 Alternative Crossovers: Sinusoidal Motion Crossover 01:12:43 Alternative Crossovers: Running Comparisons -~-~~-~~~-~~-~ Please watch: "Rust API Documentation Made Easy - Swagger + Actix Web"    • Rust API Documentation Made Easy - Swagger...   ~-~~-~~~-~~-~