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Hands-On Genetic Algorithms with Python

You're reading from   Hands-On Genetic Algorithms with Python Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781838557744
Length 346 pages
Edition 1st Edition
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Author (1):
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Eyal Wirsansky Eyal Wirsansky
Author Profile Icon Eyal Wirsansky
Eyal Wirsansky
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Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics of Genetic Algorithms
2. An Introduction to Genetic Algorithms FREE CHAPTER 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

Summary

In this chapter, you were introduced to constraint satisfaction problems, a close relative of the previously studied combinatorial optimization problems. Then, we explored three classic constraint satisfaction cases – the N-Queen problem, the nurse scheduling problem, and the graph coloring problem. For each of these problems, we followed the now-familiar process of finding an appropriate representation for a solution, creating a class that encapsulates the problem and evaluates a given solution, and creating a genetic algorithm solution that utilizes that class. We ended up with valid solutions for the problems while getting acquainted with the concept of hard constraints versus soft constraints.

So far, we have been looking into discrete search problems consisting of states and state transitions. In the next chapter, we will study search problems in a continuous...

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