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

Technical requirements

In this chapter, we will be using Python 3 with the following supporting libraries:

deap
numpy
matplotlib
seaborn

Important note

If you use the requirements.txt file provided (see Chapter 3), these libraries will already be in your environment.

In addition, we will be using the benchmark data from the Rosetta Code (https://rosettacode.org/wiki/Rosetta_Code) and TSPLIB (http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/) web pages.

The programs that will be used in this chapter can be found in this book’s GitHub repository: https://github.com/PacktPublishing/Hands-On-Genetic-Algorithms-with-Python-Second-Edition/tree/main/chapter_04. Check out the following video to see the Code in Action: https://packt.link/OEBOd

The rest of the chapter is locked

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

Table of Contents (24) Chapters

Technical requirements

Authors (1)

Personalised recommendations for you

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

Table of Contents (24) Chapters Close

Technical requirements

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you

Table of Contents (24) Chapters