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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

Optimizing the Eggholder function

The Eggholder function, depicted in the following diagram, is often used as a benchmark for function optimization algorithms. Finding the single global minimum of this function is considered a difficult task due to the large number of local minima, which give it the eggholder shape:

The Eggholder function
Source: https://en.wikipedia.org/wiki/File:Eggholder_function.pdf. Image by Gaortizg.
Licensed under Creative Commons CC BY-SA 3.0: https://creativecommons.org/licenses/by-sa/3.0/deed.en.

The function can be mathematically expressed as follows:

It is usually evaluated on the search space bounded by [-512, 512] in each dimension.

The global minimum of the function is known to be at:

x=512, y = 404.2319

Where the function's value is -959.6407.

In the next subsection, we will attempt to find the global minimum using the genetic algorithms...

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