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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

Solving the nurse scheduling problem

Imagine you are responsible for scheduling the shifts for the nurses in your hospital department for this week. There are three shifts in a day – morning, afternoon, and night – and for each shift, you need to assign one or more of the eight nurses that work in your department. If this sounds like a simple task, take a look at the list of relevant hospital rules:

  • A nurse is not allowed to work two consecutive shifts.
  • A nurse is not allowed to work more than five shifts per week.
  • The number of nurses per shift in your department should fall within the following limits:
    • Morning shift: 2–3 nurses
    • Afternoon shift: 24 nurses
    • Night shift: 12 nurses

In addition, each nurse can have shift preferences. For example, one nurse prefers to only work morning shifts, another nurse prefers to not work afternoon shifts...

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