Hands On Genetic Algorithms With Python Second Edition
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Hands On Genetic Algorithms With Python
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Author : Eyal Wirsansky
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-12
Hands On Genetic Algorithms With Python written by Eyal Wirsansky and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-12 with Computers categories.
Explore the ever-growing world of genetic algorithms to build and enhance AI applications involving search, optimization, machine learning, deep learning, NLP, and XAI using Python libraries Key Features Learn how to implement genetic algorithms using Python libraries DEAP, scikit-learn, and NumPy Take advantage of cloud computing technology to increase the performance of your solutions Discover bio-inspired algorithms such as particle swarm optimization (PSO) and NEAT Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by Eyal Wirsansky, a senior data scientist and AI researcher with over 25 years of experience and a research background in genetic algorithms and neural networks, Hands-On Genetic Algorithms with Python offers expert insights and practical knowledge to master genetic algorithms. After an introduction to genetic algorithms and their principles of operation, you’ll find out how they differ from traditional algorithms and the types of problems they can solve, followed by applying them to search and optimization tasks such as planning, scheduling, gaming, and analytics. As you progress, you’ll delve into explainable AI and apply genetic algorithms to AI to improve machine learning and deep learning models, as well as tackle reinforcement learning and NLP tasks. This updated second edition further expands on applying genetic algorithms to NLP and XAI and speeding up genetic algorithms with concurrency and cloud computing. You’ll also get to grips with the NEAT algorithm. The book concludes with an image reconstruction project and other related technologies for future applications. By the end of this book, you’ll have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence with Python.What you will learn Use genetic algorithms to solve planning, scheduling, gaming, and analytics problems Create reinforcement learning, NLP, and explainable AI applications Enhance the performance of ML models and optimize deep learning architecture Deploy genetic algorithms using client-server architectures, enhancing scalability and computational efficiency Explore how images can be reconstructed using a set of semi-transparent shapes Delve into topics like elitism, niching, and multiplicity in genetic solutions to enhance optimization strategies and solution diversity Who this book is for If you’re a data scientist, software developer, AI enthusiast who wants to break into the world of genetic algorithms and apply them to real-world, intelligent applications as quickly as possible, this book is for you. Working knowledge of the Python programming language is required to get started with this book.
Hands On Genetic Algorithms With Python Second Edition
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Author : Eyal Wirsansky
language : en
Publisher:
Release Date : 2024-07-12
Hands On Genetic Algorithms With Python Second Edition written by Eyal Wirsansky and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-12 with Computers categories.
This book will help you gain expertise in genetic algorithms, how they work, and when and how to use them to create Python-based apps.
Hands On Genetic Algorithms With Python
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Author : Eyal Wirsansky
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-01-31
Hands On Genetic Algorithms With Python written by Eyal Wirsansky and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-31 with Computers categories.
Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy Key Features Explore the ins and outs of genetic algorithms with this fast-paced guide Implement tasks such as feature selection, search optimization, and cluster analysis using Python Solve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applications Book DescriptionGenetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence. After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications. By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains.What you will learn Understand how to use state-of-the-art Python tools to create genetic algorithm-based applications Use genetic algorithms to optimize functions and solve planning and scheduling problems Enhance the performance of machine learning models and optimize deep learning network architecture Apply genetic algorithms to reinforcement learning tasks using OpenAI Gym Explore how images can be reconstructed using a set of semi-transparent shapes Discover other bio-inspired techniques, such as genetic programming and particle swarm optimization Who this book is for This book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book.
Ultimate Genetic Algorithms With Python
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Author : Indrajit Kar
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-09-22
Ultimate Genetic Algorithms With Python written by Indrajit Kar and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-22 with Computers categories.
TAGLINE Harness Genetic Algorithms to Build the Next Generation of Adaptive AI. KEY FEATURES ● Step-by-step tutorials on Genetic Algorithms, using PyGAD and DEAP. ● Real-world Genetic Algorithm applications in ML, DL, NLP, CV, and RL. ● Advanced coverage of evolutionary and metaheuristic algorithms. ● Integration of Genetic Algorithms with generative and agent-based AI systems. DESCRIPTION Genetic Algorithms (GAs) are nature-inspired optimization tools that help AI systems adapt, improve, and solve complex problems efficiently. Ultimate Genetic Algorithms with Python explains elaborately the fundamentals of GAs to practical, Python-based implementation, using PyGAD and DEAP. The book starts with a solid foundation, explaining how evolutionary principles can be applied to optimization tasks, search problems, and model improvement. You will also explore GA applications across multiple AI domains: optimizing machine learning workflows, evolving neural network architectures in deep learning, enhancing feature selection in NLP, improving performance in computer vision, and guiding exploration strategies in reinforcement learning. Each application chapter includes step-by-step coding examples, performance comparisons, and tuning techniques. The later sections focus on advanced metaheuristics, swarm intelligence, and integrating GAs with generative and agent-based AI systems. You will also learn how to design self-evolving, multi-agent frameworks, leverage swarm-based methods, and connect GAs to next-gen AI architectures such as Model Context Protocols (MCP). Thus, by the end of the book, you will have developed all the skills to design, implement, and scale GA-driven solutions for real-world AI challenges. Hence, evolve your AI solutions—start building with Genetic Algorithms today! WHAT WILL YOU LEARN ● Master the fundamentals and components of Genetic Algorithms. ● Implement GAs in Python, using PyGAD, DEAP, and PyTorch. ● Apply GAs for optimization, feature selection, and neural architecture search. ● Enhance AI workflows in ML, DL, NLP, CV, and RL with GAs. ● Explore metaheuristic and swarm-based algorithms for complex problem-solving. ● Integrate GAs into generative, multi-agent, and self-evolving AI systems. WHO IS THIS BOOK FOR? This book is tailored for data scientists, AI/ML engineers, researchers, and advanced students aiming to apply Genetic Algorithms to real-world AI challenges. It is also best suited for professionals in optimization, generative AI, and agent-based systems. Readers should have basic Python programming skills and foundational knowledge of machine learning concepts. Hence, whether you are a beginner seeking a solid foundation, or an experienced practitioner aiming to deepen your expertise in evolutionary computation, this handbook provides a practical and in-depth resource to enhance your skills, and deliver impactful AI solutions. TABLE OF CONTENTS 1. Introduction to Genetic Algorithms 2. Fundamentals of Genetic Algorithms 3. Overview of Genetic Algorithm Libraries 4. Genetic Algorithms and Their Applications 5. Foundation of Evolutionary Algorithms 6. Advanced Evolutionary Algorithms 7. Metaheuristic Optimization Algorithms 8. Application of Evolutionary Algo (GAs) and Generative Agentic AI 9. Applying Genetic Algorithm to Machine Learning 10. Applying Deep Learning to Genetic Algorithm 11. Applying Computer Vision Application to Genetic Algorithms 12. Applying NLP to Genetic Algorithms 13. Applying Reinforcement Learning to Genetic Algorithms 14. The Future of Genetic Algorithms Index
Genetic Algorithms With Python
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Author : Clinton Sheppard
language : en
Publisher:
Release Date : 2017
Genetic Algorithms With Python written by Clinton Sheppard and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Get a hands-on introduction to machine learning with genetic algorithms using Python. Step-by-step tutorials build your skills from Hello World! to optimizing one genetic algorithm with another, and finally genetic programming; thus preparing you to apply genetic algorithms to problems in your own field of expertise.Genetic algorithms are one of the tools you can use to apply machine learning to finding good, sometimes even optimal, solutions to problems that have billions of potential solutions. This book gives you experience making genetic algorithms work for you, using easy-to-follow example projects that you can fall back upon when learning to use other machine learning tools and techniques. Each chapter is a step-by-step tutorial that helps to build your skills at using genetic algorithms to solve problems using Python.Python is a high-level, low ceremony and powerful language whose code can be easily understood even by entry-level programmers. If you have experience with another programming language then you should have no difficulty learning Python by induction.ContentsChapter 1: Hello World! - Guess a password given the number of correct letters in the guess. Build a mutation engine.Chapter 2: One Max Problem - Produce an array of bits where all are 1s. Expands the engine to work with any type of gene.Chapter 3: Sorted Numbers - Produce a sorted integer array. Demonstrates handling multiple fitness goals and constraints between genes.Chapter 4: The 8 Queens Puzzle - Find safe Queen positions on an 8x8 board and then expand to NxN. Demonstrates the difference between phenotype and genotype.Chapter 5: Graph Coloring - Color a map of the United States using only 4 colors. Introduces standard data sets and working with files. Also introduces using rules to work with gene constraints.Chapter 6: Card Problem - More gene constraints. Introduces custom mutation, memetic algorithms, and the sum-of-difference technique. Also demonstrates a chromosome where the way a gene is used depends on its position in the gene array.Chapter 7: Knights Problem - Find the minimum number of knights required to attack all positions on a board. Introduces custom genes and gene-array creation. Also demonstrates local minimums and maximums.Chapter 8: Magic Squares - Find squares where all the rows, columns and both diagonals of an NxN matrix have the same sum. Introduces simulated annealing.Chapter 9: Knapsack Problem - Optimize the content of a container for one or more variables. Introduces branch and bound and variable length chromosomes.Chapter 10: Solving Linear Equations - Find the solutions to linear equations with 2, 3 and 4 unknowns. Branch and bound variation. Reinforces genotype flexibility.Chapter 11: Generating Sudoku - A guided exercise in generating Sudoku puzzles.Chapter 12: Traveling Salesman Problem (TSP) - Find the optimal route to visit cities. Introduces crossover and a pool of parents.Chapter 13: Approximating Pi - Find the two 10-bit numbers whose dividend is closest to Pi. Introduces using one genetic algorithm to tune another.Chapter 14: Equation Generation - Find the shortest equation that produces a specific result using addition, subtraction, multiplication, etc. Introduces symbolic genetic programming.Chapter 15: The Lawnmower Problem - Generate a series of instructions that cause a lawnmower to cut a field of grass. Genetic programming with control structures, objects and automatically defined functions (ADFs).Chapter 16: Logic Circuits - Generate circuits that behave like basic gates, gate combinations and finally a 2-bit adder....
Practical Genetic Algorithms
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Author : Randy L. Haupt
language : en
Publisher: John Wiley & Sons
Release Date : 2004-07-16
Practical Genetic Algorithms written by Randy L. Haupt and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-07-16 with Technology & Engineering categories.
* 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
The Practical Handbook Of Genetic Algorithms
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Author : Lance D. Chambers
language : en
Publisher: CRC Press
Release Date : 2000-12-07
The Practical Handbook Of Genetic Algorithms written by Lance D. Chambers and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-12-07 with Computers categories.
Rapid developments in the field of genetic algorithms along with the popularity of the first edition precipitated this completely revised, thoroughly updated second edition of The Practical Handbook of Genetic Algorithms. Like its predecessor, this edition helps practitioners stay up to date on recent developments in the field and provides material
Spring Meeting
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Author : American Geophysical Union. Meeting
language : en
Publisher:
Release Date : 2002
Spring Meeting written by American Geophysical Union. Meeting and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Geophysics categories.
Ultimate Genetic Algorithms With Python Build Intelligent And Adaptive Ai Systems With Genetic Algorithms In Python For Machine Learning Deep Learning And Multi Agent Domains
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Author : Indrajit Kar
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-09-22
Ultimate Genetic Algorithms With Python Build Intelligent And Adaptive Ai Systems With Genetic Algorithms In Python For Machine Learning Deep Learning And Multi Agent Domains written by Indrajit Kar and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-22 with Computers categories.
Harness Genetic Algorithms to Build the Next Generation of Adaptive AI. Key Features● Step-by-step tutorials on Genetic Algorithms, using PyGAD and DEAP.● Real-world Genetic Algorithm applications in ML, DL, NLP, CV, and RL.● Advanced coverage of evolutionary and metaheuristic algorithms.● Integration of Genetic Algorithms with generative and agent-based AI systems. Book DescriptionGenetic Algorithms (GAs) are nature-inspired optimization tools that help AI systems adapt, improve, and solve complex problems efficiently. Ultimate Genetic Algorithms with Python explains elaborately the fundamentals of GAs to practical, Python-based implementation, using PyGAD and DEAP. The book starts with a solid foundation, explaining how evolutionary principles can be applied to optimization tasks, search problems, and model improvement. You will also explore GA applications across multiple AI domains: optimizing machine learning workflows, evolving neural network architectures in deep learning, enhancing feature selection in NLP, improving performance in computer vision, and guiding exploration strategies in reinforcement learning. Each application chapter includes step-by-step coding examples, performance comparisons, and tuning techniques. The later sections focus on advanced metaheuristics, swarm intelligence, and integrating GAs with generative and agent-based AI systems. You will also learn how to design self-evolving, multi-agent frameworks, leverage swarm-based methods, and connect GAs to next-gen AI architectures such as Model Context Protocols (MCP). What you will learn● Master the fundamentals and components of Genetic Algorithms.● Implement GAs in Python, using PyGAD, DEAP, and PyTorch.● Apply GAs for optimization, feature selection, and neural architecture search.● Enhance AI workflows in ML, DL, NLP, CV, and RL with GAs.● Explore metaheuristic and swarm-based algorithms for complex problem-solving.● Integrate GAs into generative, multi-agent, and self-evolving AI systems.
Python Machine Learning By Example
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Author : Yuxi (Hayden) Liu
language : en
Publisher:
Release Date : 2019-02-28
Python Machine Learning By Example written by Yuxi (Hayden) Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-28 with Computers categories.
Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn Key Features Exploit the power of Python to explore the world of data mining and data analytics Discover machine learning algorithms to solve complex challenges faced by data scientists today Use Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects Book Description The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you're interested in ML, this book will serve as your entry point to ML. Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You'll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way. With the help of this extended and updated edition, you'll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more. By the end of the book, you'll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities. What you will learn Understand the important concepts in machine learning and data science Use Python to explore the world of data mining and analytics Scale up model training using varied data complexities with Apache Spark Delve deep into text and NLP using Python libraries such NLTK and gensim Select and build an ML model and evaluate and optimize its performance Implement ML algorithms from scratch in Python, TensorFlow, and scikit-learn Who this book is for If you're a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.