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

by Eyal Wirsansky

Overview

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

• 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

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.

• 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

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.

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Product Details

ISBN-13:	9781805121572
Publisher:	Packt Publishing
Publication date:	07/12/2024
Sold by:	Barnes & Noble
Format:	eBook
Pages:	418
File size:	8 MB

About the Author

Eyal Wirsansky is a senior data scientist, an experienced software engineer, a technology community leader, and an artificial intelligence researcher. Eyal began his software engineering career over twenty-five years ago as a pioneer in the field of Voice over IP. He currently works as a member of the data platform team at Gradle, Inc. During his graduate studies, he focused his research on genetic algorithms and neural networks. A notable result of this research is a novel supervised machine learning algorithm that integrates both approaches. In addition to his professional roles, Eyal serves as an adjunct professor at Jacksonville University, where he teaches a class on artificial intelligence. He also leads both the Jacksonville, Florida Java User Group and the Artificial Intelligence for Enterprise virtual user group, and authors the developer-focused artificial intelligence blog, ai4java.

Table of Contents

Table of Contents

1. An Introduction to Genetic Algorithms
2. Understanding the Key Components of Genetic Algorithms
3. Using the DEAP Framework
4. Combinatorial Optimization
5. Constraint Satisfaction
6. Optimizing Continuous Functions
7. Enhancing Machine Learning Models Using Feature Selection
8. Hyperparameter Tuning Machine Learning Models
9. Architecture Optimization of Deep Learning Networks
10. Reinforcement Learning with Genetic Algorithms
11. Natural Language Processing
12. Explainable AI and Counterfactuals
13. Speeding Up Genetic Algorithms with Concurrency
14. Harnessing the Cloud
15. Genetic Image Reconstruction
16. Other Evolutionary and Bio-Inspired Computation Techniques