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Hands-On Genetic Algorithms with Python

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

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781805123798
Length 418 pages
Edition 2nd 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 (24) Chapters Close

Preface 1. Part 1: The Basics of Genetic Algorithms FREE CHAPTER
2. Chapter 1: An Introduction to Genetic Algorithms 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 23. Other Books You May Enjoy

Finding the mystery word using genetic algorithms

In recent years, online mystery-word games have gained significant popularity. One standout among them is Semantle, a game that challenges you to guess the word of the day based on its meaning.

This game provides feedback on how semantically similar your guesses are to the target word and features a “Hot and Cold” meter that indicates the proximity of your guess to the secret word.

Behind the scenes, Semantle employs word embeddings, specifically Word2Vec, to represent both the mystery word and players’ guesses. It calculates the semantic similarity between them by measuring the difference between their representations: the closer the vectors, the greater the resemblance between the words. The similarity score returned by the game ranges from -100 (very different from the answer) to 100 (identical to the answer).

In the following subsections, we will create two Python programs. The first serves as a simulation...

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