Genetic Algorithms In Elixir

Author : Sean Moriarity
Publisher : Pragmatic Bookshelf
ISBN 13 : 168050830X
Total Pages : 304 pages
Book Rating : 4.07/5 ( download)

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Download or read book Genetic Algorithms in Elixir written by Sean Moriarity and published by Pragmatic Bookshelf. This book was released on 2021-01-20 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: From finance to artificial intelligence, genetic algorithms are a powerful tool with a wide array of applications. But you don't need an exotic new language or framework to get started; you can learn about genetic algorithms in a language you're already familiar with. Join us for an in-depth look at the algorithms, techniques, and methods that go into writing a genetic algorithm. From introductory problems to real-world applications, you'll learn the underlying principles of problem solving using genetic algorithms. Evolutionary algorithms are a unique and often overlooked subset of machine learning and artificial intelligence. Because of this, most of the available resources are outdated or too academic in nature, and none of them are made with Elixir programmers in mind. Start from the ground up with genetic algorithms in a language you are familiar with. Discover the power of genetic algorithms through simple solutions to challenging problems. Use Elixir features to write genetic algorithms that are concise and idiomatic. Learn the complete life cycle of solving a problem using genetic algorithms. Understand the different techniques and fine-tuning required to solve a wide array of problems. Plan, test, analyze, and visualize your genetic algorithms with real-world applications. Open your eyes to a unique and powerful field - without having to learn a new language or framework. What You Need: You'll need a macOS, Windows, or Linux distribution with an up-to-date Elixir installation.

Author : Sean Moriarity
Publisher : Pragmatic Bookshelf
ISBN 13 : 9781680507942
Total Pages : 244 pages
Book Rating : 4.4X/5 ( download)

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Download or read book Genetic Algorithms in Elixir written by Sean Moriarity and published by Pragmatic Bookshelf. This book was released on 2021-02-09 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: From finance to artificial intelligence, genetic algorithms are a powerful tool with a wide array of applications. But you don't need an exotic new language or framework to get started; you can learn about genetic algorithms in a language you're already familiar with. Join us for an in-depth look at the algorithms, techniques, and methods that go into writing a genetic algorithm. From introductory problems to real-world applications, you'll learn the underlying principles of problem solving using genetic algorithms. Evolutionary algorithms are a unique and often overlooked subset of machine learning and artificial intelligence. Because of this, most of the available resources are outdated or too academic in nature, and none of them are made with Elixir programmers in mind. Start from the ground up with genetic algorithms in a language you are familiar with. Discover the power of genetic algorithms through simple solutions to challenging problems. Use Elixir features to write genetic algorithms that are concise and idiomatic. Learn the complete life cycle of solving a problem using genetic algorithms. Understand the different techniques and fine-tuning required to solve a wide array of problems. Plan, test, analyze, and visualize your genetic algorithms with real-world applications. Open your eyes to a unique and powerful field - without having to learn a new language or framework. What You Need: You'll need a macOS, Windows, or Linux distribution with an up-to-date Elixir installation.

Author : Michael Affenzeller
Publisher : CRC Press
ISBN 13 : 1420011324
Total Pages : 395 pages
Book Rating : 4.26/5 ( download)

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Download or read book Genetic Algorithms and Genetic Programming written by Michael Affenzeller and published by CRC Press. This book was released on 2009-04-09 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for al

Author : Frances Buontempo
Publisher : Pragmatic Bookshelf
ISBN 13 : 1680506587
Total Pages : 307 pages
Book Rating : 4.87/5 ( download)

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Download or read book Genetic Algorithms and Machine Learning for Programmers written by Frances Buontempo and published by Pragmatic Bookshelf. This book was released on 2019-01-23 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.

Author : S.N. Sivanandam
Publisher : Springer Science & Business Media
ISBN 13 : 3540731903
Total Pages : 453 pages
Book Rating : 4.00/5 ( download)

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Download or read book Introduction to Genetic Algorithms written by S.N. Sivanandam and published by Springer Science & Business Media. This book was released on 2007-10-24 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a basic introduction to genetic algorithms. It provides a detailed explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. In addition, the book presents implementation of optimization problems using C and C++ as well as simulated solutions for genetic algorithm problems using MATLAB 7.0. It also includes application case studies on genetic algorithms in emerging fields.

Author : Randy L. Haupt
Publisher : John Wiley & Sons
ISBN 13 : 0471671754
Total Pages : 273 pages
Book Rating : 4.56/5 ( download)

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Download or read book Practical Genetic Algorithms written by Randy L. Haupt and published by John Wiley & Sons. This book was released on 2004-07-30 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: * This book deals with the fundamentals of genetic algorithms and their applications in a variety of different areas of engineering and science * Most significant update to the second edition is the MATLAB codes that accompany the text * Provides a thorough discussion of hybrid genetic algorithms * Features more examples than first edition

Author : Colin R. Reeves
Publisher : Springer Science & Business Media
ISBN 13 : 0306480506
Total Pages : 332 pages
Book Rating : 4.08/5 ( download)

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Download or read book Genetic Algorithms: Principles and Perspectives written by Colin R. Reeves and published by Springer Science & Business Media. This book was released on 2006-04-11 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory is a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops. However, this theoretical work is still rather fragmented, and the authors believe that it is the right time to provide the field with a systematic presentation of the current state of theory in the form of a set of theoretical perspectives. The authors do this in the interest of providing students and researchers with a balanced foundational survey of some recent research on GAs. The scope of the book includes chapter-length discussions of Basic Principles, Schema Theory, "No Free Lunch", GAs and Markov Processes, Dynamical Systems Model, Statistical Mechanics Approximations, Predicting GA Performance, Landscapes and Test Problems.

Author : Lance D. Chambers
Publisher : CRC Press
ISBN 13 : 1420050087
Total Pages : 592 pages
Book Rating : 4.80/5 ( download)

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Download or read book Practical Handbook of Genetic Algorithms written by Lance D. Chambers and published by CRC Press. This book was released on 2019-09-17 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Handbook of Genetic Algorithms, Volume 3: Complex Coding Systems contains computer-code examples for the development of genetic algorithm systems - compiling them from an array of practitioners in the field. Each contribution of this singular resource includes: unique code segments documentation descripti

Author : Kim-Fung Man
Publisher : Springer Science & Business Media
ISBN 13 : 144710577X
Total Pages : 346 pages
Book Rating : 4.70/5 ( download)

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Download or read book Genetic Algorithms written by Kim-Fung Man and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive book gives a overview of the latest discussions in the application of genetic algorithms to solve engineering problems. Featuring real-world applications and an accompanying disk, giving the reader the opportunity to use an interactive genetic algorithms demonstration program.

Author : Ivan Gridin
Publisher : BPB Publications
ISBN 13 : 8194837758
Total Pages : 330 pages
Book Rating : 4.56/5 ( download)

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Download or read book Learning Genetic Algorithms with Python written by Ivan Gridin and published by BPB Publications. This book was released on 2021-02-13 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Refuel your AI Models and ML applications with High-Quality Optimization and Search Solutions DESCRIPTION Genetic algorithms are one of the most straightforward and powerful techniques used in machine learning. This book ÔLearning Genetic Algorithms with PythonÕ guides the reader right from the basics of genetic algorithms to its real practical implementation in production environments.Ê Each of the chapters gives the reader an intuitive understanding of each concept. You will learn how to build a genetic algorithm from scratch and implement it in real-life problems. Covered with practical illustrated examples, you will learn to design and choose the best model architecture for the particular tasks. Cutting edge examples like radar and football manager problem statements, you will learn to solve high-dimensional big data challenges with ways of optimizing genetic algorithms. KEY FEATURESÊÊ _ Complete coverage on practical implementation of genetic algorithms. _ Intuitive explanations and visualizations supply theoretical concepts. _ Added examples and use-cases on the performance of genetic algorithms. _ Use of Python libraries and a niche coverage on the performance optimization of genetic algorithms. WHAT YOU WILL LEARNÊ _ Understand the mechanism of genetic algorithms using popular python libraries. _ Learn the principles and architecture of genetic algorithms. _ Apply and Solve planning, scheduling and analytics problems in Enterprise applications. _Ê Expert learning on prime concepts like Selection, Mutation and Crossover. WHO THIS BOOK IS FORÊÊ The book is for Data Science team, Analytics team, AI Engineers, ML Professionals who want to integrate genetic algorithms to refuel their ML and AI applications. No special expertise about machine learning is required although a basic knowledge of Python is expected. TABLE OF CONTENTS 1. Introduction 2. Genetic Algorithm Flow 3. Selection 4. Crossover 5. Mutation 6. Effectiveness 7. Parameter Tuning 8. Black-box Function 9. Combinatorial Optimization: Binary Gene Encoding 10. Combinatorial Optimization: Ordered Gene Encoding 11. Other Common Problems 12. Adaptive Genetic Algorithm 13. Improving Performance