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Hands-On Genetic Algorithms with Python

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

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781805123798
Length 418 pages
Edition 2nd 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 (24) Chapters Close

Preface 1. Part 1: The Basics of Genetic Algorithms FREE CHAPTER
2. Chapter 1: An Introduction to Genetic Algorithms 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 23. Other Books You May Enjoy

Solving the VRP

Imagine that you now manage a larger fulfillment center. You still need to deliver packages to a list of customers, but now, you have a fleet of several vehicles at your disposal. What’s the best way to deliver the packages to the customers using these vehicles?

This is an example of the VRP, a generalization of the TSP described in the previous section. The basic VRP consists of the following three components:

  • The list of locations that need to be visited
  • The number of vehicles
  • The location of the depot, which is used as the starting and ending point for each of the vehicles

The problem has numerous variations, such as several depot locations, time-critical deliveries, different types of vehicles (varying capacity, varying fuel consumption), and many more.

The goal of the problem is to minimize the cost, which can also be defined in many different ways. Examples include minimizing the time it takes to deliver all the packages, minimizing...

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