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

Description

Written 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.

Who is this book 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.

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

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Amazon Customer Sep 13, 2024

The book opens with a solid introduction to the principles of genetic algorithms, effectively distinguishing them from traditional algorithms. This foundational knowledge is crucial for readers who may be unfamiliar with the nuances of evolutionary computation. The author explains the types of problems GAs can solve, such as planning, scheduling, and optimization tasks, making it clear why they are a powerful tool in the data scientist's toolkit.Also the content focusing on the integration of GAs with modern AI techniques, particularly in natural language processing (NLP) and explainable AI (XAI). Genetic algorithm can enhance machine learning models, tackle reinforcement learning challenges, and optimize deep learning architectures, providing practical examples that readers can easily follow. The inclusion of advanced topics like concurrency in genetic algorithms and their deployment in cloud computing environments exemplifies the book's commitment to addressing contemporary issues in computational efficiency and scalability.

Amazon Verified review

Steven Fernandes Aug 06, 2024

The book expands into creating cutting-edge applications with reinforcement learning, NLP, and explainable AI, as well as enhancing the performance of machine learning models. It covers the deployment of genetic algorithms in client-server architectures to improve scalability and computational efficiency. Readers will also explore innovative methods like image reconstruction using semi-transparent shapes and delve into advanced genetic algorithms concepts such as elitism, niching, and multiplicity to boost optimization strategies and diversify solutions. This book is a crucial resource for anyone looking to enhance their understanding and application of genetic algorithms in AI.

Ernest Aug 04, 2024

"Hands-On Genetic Algorithms with Python" by Eyal Wirsansky stands out as an exemplary resource for anyone eager to explore the world of Genetic Algorithms (GAs). Wirsansky has crafted a comprehensive guide that caters to a wide spectrum of needs, making it an invaluable asset whether you are a student, researcher, or educator. This book brilliantly balances theoretical foundations with practical applications, providing a clear and thorough exploration of GAs.The table of contents unfolds like pieces of a puzzle, fitting together seamlessly to reveal an impressive and coherent picture of GAs. The author has included a well-organized, meticulously documented, and accessible Python code repository. This hands-on approach empowers readers to gain practical experience, enabling them to apply the techniques to their own research and projects effectively.The fact that the book has reached its second edition is a testament to its success and wide acceptance in the field. Similar to the first edition, Part 3 is the highlight, where the intersection of GAs and Artificial Intelligence (AI) is explored in depth. Topics such as Feature Selection for Machine Learning (ML) models, Hyperparameter Tuning, Architecture Optimization of Deep Learning Networks, and Reinforcement Learning with GAs are comprehensively covered, continuing to build on the solid foundation laid in the previous edition.In this new edition, Wirsansky has introduced two captivating chapters: “Natural Language Processing (NLP)”, and “Explainable AI, Causality, and Counterfactuals with Genetic Algorithms”. These additions are not only timely but also extremely impactful, given the current prominence of these topics. The discussion on counterfactuals, though concise, manages to be both informative and profound, providing readers with a nuanced understanding of its applications. I eagerly anticipate the third edition, hoping to see more examples of GAs applied to XAI and Causality.A notable addition to this edition is the chapter on Enhancing Performance with Concurrency and Cloud Strategies. This is particularly relevant for professionals dealing with big data or projects that demand swift execution. It introduces a new dimension to the book, equipping readers with strategies to handle computational challenges efficiently.The final chapter offers a glimpse into other evolutionary and bio-inspired computation methods, serving as a valuable guide for fundamental researchers and curious learners looking to expand their knowledge beyond Genetic Algorithms. This "where-to-go" section opens new avenues for exploration and study.Looking forward, it would be beneficial for future editions to address the (current) limitations of GAs. Given Eyal Wirsansky's expertise in both GAs and Deep Learning (DL), an exploration of how GAs could potentially revolutionize DL in the future would be particularly fascinating and insightful.

Om S Jul 31, 2024

Eyal Wirsansky's "Hands-On Genetic Algorithms with Python" is an excellent resource for mastering genetic algorithms using Python. It addresses topics like search, optimization, machine learning, and deep learning. The guide demonstrates how to leverage cloud computing for improved performance and covers bio-inspired algorithms such as PSO and NEAT. Ideal for data scientists and AI enthusiasts with Python skills, it includes practical examples and projects. This book is a valuable tool for anyone looking to implement genetic algorithms in real-world AI applications.

Karthik Rajashekaran Jul 30, 2024

Hands-On Genetic Algorithms with Python by Eyal Wirsansky is an essential resource for anyone looking to delve into the world of genetic algorithms (GAs) using Python. This comprehensive guide bridges the gap between theory and practical application, making complex concepts accessible to both beginners and seasoned developers.The book starts with the basics of genetic algorithms, explaining their biological inspiration and fundamental components like selection, crossover, mutation, and fitness functions. It then moves on to practical implementation, providing clear and well-structured Python examples that allow readers to see the theory in action.One of the standout features of this book is its focus on real-world applications. Wirsansky does an excellent job of demonstrating how GAs can be used to solve complex optimization problems, from scheduling and routing to machine learning. The inclusion of advanced topics such as multi-objective optimization, parallel GAs, and hybrid algorithms ensures that readers are well-equipped to tackle a wide range of challenges.The writing is clear and engaging, with a logical progression that makes learning straightforward. Each chapter builds on the previous one, reinforcing concepts and enhancing understanding. The practical exercises and examples are particularly valuable, providing hands-on experience that is crucial for mastering genetic algorithms.Overall, Hands-On Genetic Algorithms with Python is a must-read for data scientists, researchers, and developers interested in evolutionary algorithms. It provides the knowledge and tools needed to harness the power of genetic algorithms for solving real-world problems efficiently and effectively.Pros:- Clear and concise explanations of genetic algorithm concepts.- Practical Python examples that bridge theory and application.- Coverage of advanced topics for comprehensive learning.- Focus on real-world applications.Cons:Requires a basic understanding of Python programming.

Hands-On Genetic Algorithms with Python: Apply genetic algorithms to solve real-world AI and machine learning problems , Second Edition

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