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

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781805123798

Length 418 pages

Edition 2nd Edition

Languages

Python

Tools

Jupyter

Concepts

Data Science

Author (1):

Eyal Wirsansky

View More author details

Table of Contents (24) Chapters

Preface

1. Part 1: The Basics of Genetic Algorithms

2. Chapter 1: An Introduction to Genetic Algorithms FREE CHAPTER

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

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Solving the OneMax problem with DEAP

In the previous chapter, we mentioned several choices that need to be made when solving a problem using the genetic algorithm approach. As we tackle the OneMax problem, we will make these choices in a series of steps. In the chapters to follow, we will keep using the same series of steps as we apply the genetic algorithms approach to various types of problems.

Choosing the chromosome

Since the OneMax problem deals with binary strings, the choice of chromosome is easy – each individual will be represented with a binary string that directly represents a candidate solution. In the actual Python implementation, this will be implemented as a list containing integer values of either 0 or 1. The length of the chromosome matches the size of the OneMax problem. For example, for a OneMax problem of size 5, the 10010 individual will be represented by [1, 0, 0, 1, 0].