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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Crossover methods

The crossover operator, also referred to as recombination, corresponds to the crossover that takes place during sexual reproduction in biology and is used to combine the genetic information of two individuals, serving as parents, to produce (usually two) offspring.

The crossover operator is typically applied with some (high) probability value. Whenever crossover is not applied, both parents are directly cloned into the next generation.

The following sections describe some of the commonly used crossover methods and their typical use cases. However, in certain situations, you may opt to use a problem-specific crossover method that will be more suitable for a particular case.

Single-point crossover

In the single-point crossover method, a location on the chromosomes of both parents is selected randomly. This location is referred to as the crossover point or cut point. Genes to the right of that point are swapped between the two parent chromosomes. As a result...