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Hands-On Genetic Algorithms with Python

You're reading from  Hands-On Genetic Algorithms with Python

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781838557744
Pages 346 pages
Edition 1st Edition
Languages
Author (1):
Eyal Wirsansky Eyal Wirsansky
Profile icon Eyal Wirsansky

Table of Contents (18) Chapters

Preface 1. Section 1: The Basics of Genetic Algorithms
2. An Introduction to Genetic Algorithms 3. Understanding the Key Components of Genetic Algorithms 4. Section 2: Solving Problems with Genetic Algorithms
5. Using the DEAP Framework 6. Combinatorial Optimization 7. Constraint Satisfaction 8. Optimizing Continuous Functions 9. Section 3: Artificial Intelligence Applications of Genetic Algorithms
10. Enhancing Machine Learning Models Using Feature Selection 11. Hyperparameter Tuning of Machine Learning Models 12. Architecture Optimization of Deep Learning Networks 13. Reinforcement Learning with Genetic Algorithms 14. Section 4: Related Technologies
15. Genetic Image Reconstruction 16. Other Evolutionary and Bio-Inspired Computation Techniques 17. Other Books You May Enjoy

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...

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