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

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23. Other Books You May Enjoy

Multiprocessing using the SCOOP library

Another approach to introduce multiprocessing is by utilizing SCOOP, a Python library designed to parallelize and distribute code execution across multiple processes. SCOOP, which stands for Simple COncurrent Operations in Python, provides a straightforward interface for parallel computing in Python, which we’ll explore shortly.

Applying SCOOP to a DEAP-based program is quite similar to using the multiprocessing.Pool module, as demonstrated by the Python 05_one_max_scoop.py program, available at https://github.com/PacktPublishing/Hands-On-Genetic-Algorithms-with-Python-Second-Edition/blob/main/chapter_13/05_one_max_scoop.py.

This program requires only a couple of modifications to the original non-multiprocessing version of the 02_one_max_busy.py program; these are outlined here:

Import SCOOP’s futures module:
```
from scoop import futures
```
Register the map method of SCOOP’s futures module as the “map”...

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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

Multiprocessing using the SCOOP library

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

Multiprocessing using the SCOOP library

Authors (1)

Personalised recommendations for you

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