Hands-On Genetic Algorithms with Python, Second Edition

eBook Details:

• Paperback: 418 pages
• Publisher: WOW! eBook; 2nd edition (July 12, 2024)
• Language: English
• ISBN-10: 1805123793
• ISBN-13: 978-1805123798

eBook Description:

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

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, 2nd Edition 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 Hands-On Genetic Algorithms with Python, Second Edition book concludes with an image reconstruction project and other related technologies for future applications.

• 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

By the end of this Hands-On Genetic Algorithms with Python, 2nd Edition book, you’ll have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence with Python.