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Hands-On Genetic Algorithms with Python

You're reading from Hands-On Genetic Algorithms with Python Apply genetic algorithms to solve real-world AI and machine learning problems

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781805123798

Length 418 pages

Edition 2nd Edition

Languages

Python

Tools

Jupyter

Concepts

Data Science

Author (1):

Eyal Wirsansky

View More author details

Table of Contents (24) Chapters

Preface

1. Part 1: The Basics of Genetic Algorithms

2. Chapter 1: An Introduction to Genetic Algorithms FREE CHAPTER

3. Chapter 2: Understanding the Key Components of Genetic Algorithms

4. Part 2: Solving Problems with Genetic Algorithms

5. Chapter 3: Using the DEAP Framework

6. Chapter 4: Combinatorial Optimization

7. Chapter 5: Constraint Satisfaction

8. Chapter 6: Optimizing Continuous Functions

9. Part 3: Artificial Intelligence Applications of Genetic Algorithms

10. Chapter 7: Enhancing Machine Learning Models Using Feature Selection

11. Chapter 8: Hyperparameter Tuning of Machine Learning Models

12. Chapter 9: Architecture Optimization of Deep Learning Networks

13. Chapter 10: Reinforcement Learning with Genetic Algorithms

14. Chapter 11: Natural Language Processing

15. Chapter 12: Explainable AI, Causality, and Counterfactuals with Genetic Algorithms

16. Part 4: Enhancing Performance with Concurrency and Cloud Strategies

17. Chapter 13: Accelerating Genetic Algorithms – the Power of Concurrency

18. Chapter 14: Beyond Local Resources – Scaling Genetic Algorithms in the Cloud

19. Part 5: Related Technologies

20. Chapter 15: Evolutionary Image Reconstruction with Genetic Algorithms

21. Chapter 16: Other Evolutionary and Bio-Inspired Computation Techniques

22. Index

Why subscribe?

23. Other Books You May Enjoy

Further reading

For more information, please refer to the following resources:

Mathematical optimization: finding minima of functions:
http://scipy-lectures.org/advanced/mathematical_optimization/
Optimization Test Functions and Datasets:
https://www.sfu.ca/~ssurjano/optimization.html
Introduction to Constrained Optimization:
https://web.stanford.edu/group/sisl/k12/optimization/MO-unit3-pdfs/3.1introandgraphical.pdf
Constraint handling in DEAP:
https://deap.readthedocs.io/en/master/tutorials/advanced/constraints.html

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Authors (1)

Eyal Wirsansky

Eyal Wirsansky

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.

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