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

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838557744

Length 346 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Artificial Intelligence

Author (1):

Eyal Wirsansky

View More author details

Table of Contents (18) Chapters

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

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Technical requirements

In this chapter, we will be using Python 3 with the following supporting libraries:

deap
numpy
matplotlib
seaborn

In addition, we will be using the benchmark data from the Rosetta Code Knapsack problem/0-1 (http://rosettacode.org/wiki/Knapsack_problem/0-1), and the TSP LIB (http://elib.zib.de/pub/mp-testdata/tsp/tsplib/tsp/).

The programs used in this chapter can be found in the book's GitHub repository at https://github.com/PacktPublishing/Hands-On-Genetic-Algorithms-with-Python/tree/master/Chapter04.

Check out the following video to see the Code in Action:
http://bit.ly/2tfyfMI

The rest of the chapter is locked

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Authors (1)

Eyal Wirsansky

Eyal Wirsansky is a senior data scientist, an experienced software engineer, a technology community leader, and an artificial intelligence researcher. Eyal began his software engineering career over twenty-five years ago as a pioneer in the field of Voice over IP. He currently works as a member of the data platform team at Gradle, Inc. During his graduate studies, he focused his research on genetic algorithms and neural networks. A notable result of this research is a novel supervised machine learning algorithm that integrates both approaches. In addition to his professional roles, Eyal serves as an adjunct professor at Jacksonville University, where he teaches a class on artificial intelligence. He also leads both the Jacksonville, Florida Java User Group and the Artificial Intelligence for Enterprise virtual user group, and authors the developer-focused artificial intelligence blog, ai4java.

See other products by Eyal Wirsansky