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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

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 is the best way to deliver the packages to the customers using these vehicles?

This is an example of 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 one of the vehicles

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

The goal of the problem is to minimize the cost, which can also be defined in many...

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